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• Network Agnostic Perfectly Secure Multiparty Computation Against General Adversaries
  crossref Ananya Appan, Anirudh Chandramouli, Ashish Choudhury 2024-09-30T17:25:46Z 置信度 0.70
• STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING
  crossref W. Priesnitz Filho, C. N. C. Ribeiro 2018-01-01T17:36:39Z 置信度 0.70
• Distributed Proxy Re- Encryption Protocol for Secure Multiparty Computation with Fully Homomorphic Encryption
  crossref Busranur Bulbul Demir, Deniz Turgay Altilar 2024-09-02T17:34:13Z 置信度 0.70
• Enhanced Security using Secure Multiparty Computation to E-voting application in cloud
  Secure outsourcing of computation on sensitive data is an important topic that has received a lot of attention recently. E-Voting system is interested in method collaboration system to ameliorate the quality of System. Cloud computing is integrating in communi…
  crossref Umang Kishor Chaudhari, A Vijaya Kumar, G Venkata Sai 2018-05-13T16:22:08Z 置信度 0.70
• Secure Multiparty Protocols on Blockchain with Fairness and Scalability
  Secure multiparty computation is a major field of research in modern cryptography. It allows for the creation of a protocol that maintains the privacy of the inputs and ensures that violation of the protocol results in no undue benefit to the violator or detri…
  crossref Joshua Holmes 2025-01-28T19:13:50Z 置信度 0.70
• Quantum Secure Multiparty Computation with Symmetric Boolean Functions*
  We propose a class of n -variable Boolean functions which can be used to implement quantum secure multiparty computation. We also give an implementation of a special quantum secure multiparty computation protocol. An advantage of our protocol is that only 1 qu…
  crossref Hao Cao, Wenping Ma, Ge Liu, Liangdong Lü 等 2020-05-12T12:23:13Z 置信度 0.70
• Privacy-Preserving Biometric Identification Using Secure Multiparty Computation: An Overview and Recent Trends
  crossref Julien Bringer, Herve Chabanne, Alain Patey 2013-02-13T19:14:39Z 置信度 0.70
• Zero-Knowledge Proofs from Secure Multiparty Computation
  A zero-knowledge proof allows a prover to convince a verifier of an assertion without revealing any further information beyond the fact that the assertion is true. Secure multiparty computation allows n mutually suspicious players to jointly compute a function…
  crossref Yuval Ishai, Eyal Kushilevitz, Rafail Ostrovsky, Amit Sahai 2009-09-02T18:07:04Z 置信度 0.70
• Comment on “Secure multiparty computation with a dishonest majority via quantum means”
  crossref Yan-bing Li, Qiao-yan Wen, Su-juan Qin 2011-07-27T10:51:07Z 置信度 0.70
• Unique Software Engineering Techniques: Panacea for Threat Complexities in Secure Multiparty Computation (MPC) with Big Data
  Most large corporations with big data have adopted more privacy measures in handling their sensitive/private data and as a result, employing the use of analytic tools to run across multiple sources has become ineffective. Joint computation across multiple part…
  crossref Uchechukwu Emejeamara, Udochukwu Nwoduh, Andrew Madu 2020-11-22T07:08:42Z 置信度 0.70
• MPC-XGB: Privacy-Preserving Vertical Federated XGBoost via Secure Multiparty Computation
  crossref Asma Ramay, Estrid He, Mengmeng Yang, Tabinda Sarwar 等 2026-03-06T20:57:57Z 置信度 0.70
• On Definitions for Semi-Honest Secure Multiparty Computation
  crossref Koji NUIDA 2025-06-24T18:09:11Z 置信度 0.70
• Multiparty computation secure against continual memory leakage
  crossref Elette Boyle, Shafi Goldwasser, Abhishek Jain, Yael Tauman Kalai 2012-05-21T15:20:35Z 置信度 0.70
• Secure Multiparty Computation between Distrusted Networks Terminals
  crossref S.-C. S. Cheung, Thinh Nguyen 2007-12-13T18:41:26Z 置信度 0.70
• Elementary secure-multiparty computation for massive-scale collaborative network monitoring: A quantitative assessment
  crossref A. Iacovazzi, A. D’Alconzo, F. Ricciato, M. Burkhart 2013-09-04T03:00:35Z 置信度 0.70
• Notice of Violation of IEEE Publication Principles - A Study on Secure Multiparty Computation Problems and Their Relevance
  crossref Zulfa Shaikh, Durgesh Kumar Mishra 2011-01-28T15:12:36Z 置信度 0.70
• Round Efficient Unconditionally Secure Multiparty Computation Protocol
  crossref Arpita Patra, Ashish Choudhary, C. Pandu Rangan 2008-11-26T12:11:54Z 置信度 0.70
• USING JIFF FOR COLLABORATIVE MEDICAL DATA ANALYSIS WITH SECURE MULTIPARTY COMPUTATION
  crossref Usha Divakarla, K Chandrasekaran, K Hemanth Kumar Reddy 2025-03-19T10:35:16Z 置信度 0.70
• Quorums Quicken Queries: Efficient Asynchronous Secure Multiparty Computation
  crossref Varsha Dani, Valerie King, Mahnush Movahedi, Jared Saia 2014-01-02T11:08:11Z 置信度 0.70
• A Glimpse of Secure Multiparty Computation for Privacy Preserving Data Mining
  crossref Mamta Sakpal 2019-01-30T05:09:03Z 置信度 0.70
• Delegated Proof of Secret Sharing: A Privacy-Preserving Consensus Protocol Based on Secure Multiparty Computation for IoT Environment
  With the rapid advancement and wide application of blockchain technology, blockchain consensus protocols, which are the core part of blockchain systems, along with the privacy issues, have drawn much attention from researchers. A key aspect of privacy in the b…
  crossref Tieming Geng, Laurent Njilla, Chin-Tser Huang 2022-01-25T21:07:11Z 置信度 0.70
• Secure multiparty quantum computation protocol for quantum circuits: The exploitation of triply even quantum error-correcting codes
  crossref Petr A. Mishchenko, Keita Xagawa 2024-08-28T11:35:35Z 置信度 0.70
• Secure Multiparty/Multicandidate Electronic Elections
  In this chapter we present a methodology for proving in Zero Knowledge the validity of selecting a subset of a set belonging to predefined family of sets. We apply this methodology in electronic voting to provide for extended ballot options. Our proposed votin…
  crossref Tassos Dimitriou, Dimitris Foteinakis 2011-05-24T12:56:28Z 置信度 0.70
• A Secure Priority Queue; Or: On Secure Datastructures from Multiparty Computation
  crossref Tomas Toft 2014-10-18T11:14:47Z 置信度 0.70
• High Efficiency Secure Channels for a Secure Multiparty Computation Protocol Based on Signal
  Secure multiparty computation (MPC) requires the messages transmitted in secure channels which can provide encryption and authorization to the messages. To implement a secure channel for MPC protocols, researchers have tried some communication protocols, such …
  crossref Yunqi Yang, Rui Zhang 2023-04-19T20:20:19Z 置信度 0.70
• Breaking the ${\mathcal{O}}(n|C|)$ Barrier for Unconditionally Secure Asynchronous Multiparty Computation
  crossref Ashish Choudhury 2013-12-02T00:20:12Z 置信度 0.70
• Efficient lookup-table protocol in secure multiparty computation
  Secure multiparty computation (SMC) permits a collection of parties to compute a collaborative result, without any of the parties gaining any knowledge about the inputs provided by other parties. Specifications for SMC are commonly presented as boolean circuit…
  crossref John Launchbury, Iavor S. Diatchki, Thomas DuBuisson, Andy Adams-Moran 2012-11-20T15:50:20Z 置信度 0.70
• Efficient lookup-table protocol in secure multiparty computation
  crossref John Launchbury, Iavor S. Diatchki, Thomas DuBuisson, Andy Adams-Moran 2012-09-12T09:01:27Z 置信度 0.70
• Quantum Secure Multiparty Computation of Phylogenetic Trees of SARS-CoV-2 Genome
  crossref Manuel B. Santos, Ana C. Gomes, Armando N. Pinto, Paulo Mateus 2021-05-27T00:22:31Z 置信度 0.70
• Conditionally Secure Multiparty Computation using Secret Sharing Scheme for n < 2k-1 (Short Paper)
  crossref Ahmad Akmal Aminuddin Mohd Kamal, Keiichi Iwamura 2018-10-30T10:06:33Z 置信度 0.70
• Erratum: Secure multiparty quantum computation with few qubits [Phys. Rev. A <b>102</b> , 022405 (2020)]
  crossref Victoria Lipinska, Jérémy Ribeiro, Stephanie Wehner 2024-07-02T14:02:23Z 置信度 0.70
• Congestion Control during Data Privacy in Secure Multiparty Computation
  crossref Zulfa Shaikh, Dinesh Bhati, D. M. Puntambekar, Pushpa Pathak 等 2010-01-26T17:42:18Z 置信度 0.70
• On the (In)Efficiency of Non-Interactive Secure Multiparty Computation
  crossref Maki Yoshida, Satoshi Obana 2016-03-09T09:15:22Z 置信度 0.70
• Performance Impact Analysis of Rounds and Amounts of Communication in Secure Multiparty Computation Based on Secret Sharing
  crossref Diana-Elena Falamas, Kinga Marton 2019-11-26T01:28:38Z 置信度 0.70
• Application of SMPC (Secure Multiparty Computation) for Privacy Protection in MyData Environment
  crossref Ji Yeon Lee, Soon Seok Kim 2023-12-29T02:15:00Z 置信度 0.70
• Fair and Efficient Secure Multiparty Computation with Reputation Systems
  crossref Gilad Asharov, Yehuda Lindell, Hila Zarosim 2013-11-23T03:53:33Z 置信度 0.70
• Initial Public Offering (IPO) on Permissioned Blockchain Using Secure Multiparty Computation
  crossref Tzipora Halevi, Fabrice Benhamouda, Angelo De Caro, Shai Halevi 等 2020-01-03T01:12:17Z 置信度 0.70
• Secure Multiparty Computing: A Decentralized Approach to GPA Calculation
  crossref Sayed Mohammad Badiezadegan 2023-09-13T06:28:02Z 置信度 0.70
• Secure Multiparty Quantum Computation with (Only) a Strict Honest Majority
  crossref Michael Ben-Or, Claude Crepeau, Daniel Gottesman, Avinatan Hassidim 等 2006-12-20T16:08:18Z 置信度 0.70
• Distributed Ledgers and Secure Multiparty Computation for Financial Reporting and Auditing
  To understand the disruption and implications of distributed ledger technologies for financial reporting and auditing, we analyze firm misreporting, auditor monitoring and competition, and regulatory policy in a unified model. A federated blockchain for financ…
  crossref Sean Shun Cao, Lin William Cong, Baozhong Yang 2024-08-22T14:43:41Z 置信度 0.70
• IMPROVEMENT IN ROUTING TECHNIQUES IN P2P NETWORKS USING A CLOUD SERVICE INTERFACE WITH SECURE MULTIPARTY COMPUTATION
  crossref Anil Saroliya, Upendra Mishra, Ajay Rana 2016-09-16T11:10:46Z 置信度 0.70
• Perfectly Secure Multiparty Computation and the Computational Overhead of Cryptography
  crossref Ivan Damgård, Yuval Ishai, Mikkel Krøigaard 2010-05-19T09:16:46Z 置信度 0.70
• Secure Multiparty Computation of Approximations
  crossref Joan Feigenbaum, Yuval Ishai, Tal Malkin, Kobbi Nissim 等 2007-10-28T06:29:04Z 置信度 0.70
• Safe Data Technologies Safely Expanding Access to Administrative Tax Data
  crossref Claire Bowen, Leonard E. Burman, Robert McClelland, Aaron R. Williams 2024-08-23T14:36:41Z 置信度 0.70
• Efficient and Secure Multiparty Querying over Federated Graph Databases
  crossref Nouf Aljuaid, Alexei Lisitsa, Sven Schewe 2024-07-13T16:48:14Z 置信度 0.70
• Near-Linear Unconditionally-Secure Multiparty Computation with a Dishonest Minority
  crossref Eli Ben-Sasson, Serge Fehr, Rafail Ostrovsky 2012-08-06T06:40:28Z 置信度 0.70
• A proposal of privacy preserving reinforcement learning for secure multiparty computation
  Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attrac…
  crossref Hirofumi Miyajima, Noritaka Shigei, Syunki Makino, Hiromi Miyajima 等 2017-05-24T01:15:59Z 置信度 0.70
• Secure Multiparty Computation between Distrusted Networks Terminals
  crossref S-CS Cheung, Thinh Nguyen 2011-09-21T15:39:56Z 置信度 0.70
• Secure multiparty computation of approximations
  Approximation algorithms can sometimes provide efficient solutions when no efficient exact computation is known. In particular, approximations are often useful in a distributed setting where the inputs are held by different parties and may be extremely large. …
  crossref Joan Feigenbaum, Yuval Ishai, Tal Malkin, Kobbi Nissim 等 2006-10-18T18:11:32Z 置信度 0.70
• Privacy Protection and Query of Industrial Park Energy Usage Data Based on Searchable Encryption and Secure Multiparty Computation
  crossref Yin Wang, Jianjiang Su, Feng Jin 2024-04-02T18:38:46Z 置信度 0.70
• Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation
  crossref A. Ponmalar, Rajkumar Pandiarajan, I. Sudha, P.S. Ramesh 等 2025-02-28T18:38:44Z 置信度 0.70
• Secure multiparty quantum computation with few qubits
  crossref Victoria Lipinska, Jérémy Ribeiro, Stephanie Wehner 2020-08-07T21:46:41Z 置信度 0.70
• Combining Differential Privacy and Secure Multiparty Computation
  crossref Martin Pettai, Peeter Laud 2015-12-11T17:06:08Z 置信度 0.70
• A Domain-Specific Language for Low-Level Secure Multiparty Computation Protocols
  crossref Peeter Laud, Jaak Randmets 2015-10-06T15:22:12Z 置信度 0.70
• Efficient Secure Multiparty Computation Protocol in Asynchronous Network
  crossref Zheng Huang, Weidong Qiu, Qiang Li, Kefei Chen 2009-06-17T14:22:04Z 置信度 0.70
• ReplayMPC: A Fast Failure Recovery Protocol for Secure Multiparty Computation Applications using Blockchain
  crossref Oscar G. Bautista, Kemal Akkaya, Soamar Homsi 2023-08-07T17:55:53Z 置信度 0.70
• Graph Design for Secure Multiparty Computation over Non-Abelian Groups
  crossref Xiaoming Sun, Andrew Chi-Chih Yao, Christophe Tartary 2008-12-01T02:27:00Z 置信度 0.70
• Federated reservoir intelligence: reference implementation for distributed temporal and neuromorphic learning
  Reference implementation accompanying the Article 'Federated reservoir intelligence for distributed temporal and neuromorphic learning' (Communications Engineering, Nature Portfolio, 2026). Contents: ESN/LSM reservoir construction; statistics- and readout-base…
  datacite Dong, Liang 2026 置信度 0.66
• Federated reservoir intelligence: reference implementation for distributed temporal and neuromorphic learning
  Reference implementation accompanying the Article 'Federated reservoir intelligence for distributed temporal and neuromorphic learning' (Communications Engineering, Nature Portfolio, 2026). Contents: ESN/LSM reservoir construction; statistics- and readout-base…
  datacite Dong, Liang 2026 置信度 0.66
• A Landscape Classification Framework for NP-Hard Problems: From Reconnaissance to Algorithm Selection
  We propose a unified classification framework for NP-hard problems based on loss landscape topology. The framework classifies 95% of known NP problems into seven boxes (combinatorial optimisation, graph theory, logical satisfaction, path/network, permutation/a…
  datacite Rao, Huiying 2026 置信度 0.66 NP-hardloss landscapealgorithm selectionfitness landscape analysiscombinatorial optimisation
• Autonomous Procurement Systems for Resilient Global Supply Networks
  Abstract Global supply chains are increasingly exposed to disruptions arising from geopolitical conflicts, trade sanctions, climate-related events, logistics bottlenecks, supplier financial instability, and evolving ESG requirements. Traditional procurement sy…
  datacite Banerjee, Somnath, Bhaduri, Subhamoy, Mukherjee, Binayak 2026 置信度 0.66 Procurement AISupply Chain ResilienceGraph AIDigital TwinOperations Research
• Autonomous Procurement Systems for Resilient Global Supply Networks
  Abstract Global supply chains are increasingly exposed to disruptions arising from geopolitical conflicts, trade sanctions, climate-related events, logistics bottlenecks, supplier financial instability, and evolving ESG requirements. Traditional procurement sy…
  datacite Banerjee, Somnath, Bhaduri, Subhamoy, Mukherjee, Binayak 2026 置信度 0.66 Procurement AISupply Chain ResilienceGraph AIDigital TwinOperations Research
• FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning
  Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillation-based FL, each client applies its local model on an unla…
  datacite Ye, Wenxuan, Ayan, Onur, An, Xueli, Carle, Georg 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data
  Federated Learning (FL) enables distributed training of machine learning models while preserving data privacy. However, FL struggles with heterogeneous, non-IID client data distributions, resulting in sub-optimal and biased global models. In this paper, we pro…
  datacite Glitza, Rene, Becker, Luca, Martin, Rainer 2026 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)Sound (cs.SD)Audio and Speech Processing (eess.AS)Signal Processing (eess.SP)
• Joint Beamforming Design and Port Selection in Fluid Antenna-Assisted Multi-Cell Networks: A Personalized Federated Learning Approach
  This paper investigates joint beamforming and port selection in multi-cell fluid antenna-assisted (FAS) networks. In such networks, active beamforming and discrete FA port selection are coupled through intra-cell and inter-cell interference and are jointly opt…
  datacite Gao, Liwen, Zheng, Li, Hao, Xing, Chen, Ziru 等 2026 置信度 0.66 Information Theory (cs.IT)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection
  Reliable access selection in dynamic and heterogeneous indoor-outdoor environments is challenging because instantaneous radio measurements alone cannot capture future QoS degradation caused by mobility, blockage, traffic load, and resource competition. This pa…
  datacite Van Thieu, Nguyen, Nguyen, Ti Ti, Aouedi, Ons, Huruy, Zerihun 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences68T05, 68M10
• Rethinking the Transferable Adversarial Attacks and Robust Defense in Federated Learning
  The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machine learning models. However, the involvement of a large number of users in FL also creates open opportunities…
  datacite Xiong, Zuobin, Mukherjee, Deval, Cho, Homook, Li, Wei 2026 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Detailed Technical Embodiments and Reference Algorithms for Local Stress–Admissibility Coupling Methods in Network-Structured Systems
  Defensive Publication · Extended Technical Embodiments Companion document to DTD-P02-2026.05.29 — adding domain-specific algorithms, system architectures, and detailed embodiments for prior-art purposes. This document extends and is to be read together with th…
  datacite Melegh, Janos Gabor 2026 置信度 0.66
• Detailed Technical Embodiments and Reference Algorithms for Local Stress–Admissibility Coupling Methods in Network-Structured Systems
  Defensive Publication · Extended Technical Embodiments Companion document to DTD-P02-2026.05.29 — adding domain-specific algorithms, system architectures, and detailed embodiments for prior-art purposes. This document extends and is to be read together with th…
  datacite Melegh, Janos Gabor 2026 置信度 0.66
• Sānér, The Recursive Trinity Structure for Task Regions
  Abstract Sānér establishes a recursive control architecture for task regions by placing topology, self-modeling scheduling, and certificate-governed execution under one executable resource semantics. The nine-node construction supports exact recursive expansio…
  datacite Bsmpx 2026 置信度 0.66 Sānérrecursive task regionsnine-node cubic topologybalanced-ternary addressingself-modeling scheduling
• Sānér, The Recursive Trinity Structure for Task Regions
  Abstract Sānér establishes a recursive control architecture for task regions by placing topology, self-modeling scheduling, and certificate-governed execution under one executable resource semantics. The nine-node construction supports exact recursive expansio…
  datacite Bsmpx 2026 置信度 0.66 Sānérrecursive task regionsnine-node cubic topologybalanced-ternary addressingself-modeling scheduling
• WAA-A1/privacy-preserving-federated-ids: Updated Thesis Implementation – Version 2.0
  Updated implementation and experimental results for the 2026 Thesis. This release contains the updated federated intrusion detection implementation, experimental CSV results, generated figures, and documentation. The updated version includes evaluations of pri…
  datacite WAA-A1 2026 置信度 0.66
• Does Heavy-Tailed Gradient Noise Explain the CIFAR-10 Gap in Byzantine-Robust, Differentially Private Federated Learning? An Empirical Stress Test of Byz-Clip21-SGD2M
  Byz-Clip21-SGD2M (Islamov et al., 2026) provides high-probability convergence guarantees for federated learning under simultaneous Byzantine adversaries and differential-privacy (DP) noise, relaxing the bounded-gradient assumption behind the tight privacy-robu…
  datacite S Varughese, Johan 2026 置信度 0.66 federated learningdifferential privacyByzantine robustnessdistributed optimizationrobust aggregation
• Does Heavy-Tailed Gradient Noise Explain the CIFAR-10 Gap in Byzantine-Robust, Differentially Private Federated Learning? An Empirical Stress Test of Byz-Clip21-SGD2M
  Byz-Clip21-SGD2M (Islamov et al., 2026) provides high-probability convergence guarantees for federated learning under simultaneous Byzantine adversaries and differential-privacy (DP) noise, relaxing the bounded-gradient assumption behind the tight privacy-robu…
  datacite S Varughese, Johan 2026 置信度 0.66 federated learningdifferential privacyByzantine robustnessdistributed optimizationrobust aggregation
• HemaFAIR & HELIOS Federated Analysis Hackathon (Cyprus, May 2026): Training Resources and Datasets
  This Zenodo record contains the training and educational materials developed and used during the HemaFAIR and HELIOS Federated Data Analysis Training School and Hackathon, held at Alion Beach Hotel in Ayia Napa, Cyprus, on 11–14 May 2026. The event brought tog…
  datacite Waagmeester, Andra, Kersloot, Martijn G., Cremonesi, Francesco, Wijnbergen, Daphne 等 2026 置信度 0.66 Federated LearningHackathonTraining MaterialsHELIOSHemaFAIR
• HemaFAIR & HELIOS Federated Analysis Hackathon (Cyprus, May 2026): Training Resources and Datasets
  This Zenodo record contains the training and educational materials developed and used during the HemaFAIR and HELIOS Federated Data Analysis Training School and Hackathon, held at Alion Beach Hotel in Ayia Napa, Cyprus, on 11–14 May 2026. The event brought tog…
  datacite Waagmeester, Andra, Kersloot, Martijn G., Cremonesi, Francesco, Wijnbergen, Daphne 等 2026 置信度 0.66 Federated LearningHackathonTraining MaterialsHELIOSHemaFAIR
• Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning
  Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior. Distance-based aggregation rules, such as Krum and Multi-Krum, select updates that are closest to the majority under the assumption that ben…
  datacite Subramanian, Srinivasan, Khan, Md. Abdullah Al Hafiz, Islam, Kazi Aminul 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• Understanding Federated Learning Through the Lens of Mechanism Design: The Role of Data Heterogeneity
  Federated learning (FL) requires effective incentive mechanisms to motivate data sharing and prevent strategic free-riding. Recent FL mechanisms such as the Shapley value mechanism M^Shap guarantee reciprocal fairness for agents. However, a complete analysis o…
  datacite Alkarmi, Lina, Chen, Po-Yen, Liu, Mingyan 2026 置信度 0.66 Computer Science and Game Theory (cs.GT)Social and Information Networks (cs.SI)FOS: Computer and information sciences
• Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe
  Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interaction. Motivated by participation privacy, we study single-edit neighboring user streams, where one insertion/d…
  datacite Chan, T-H. Hubert, Shi, Elaine, Zhao, Mengshi, Zhou, Mingxun 2026 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciences
• ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
  Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surface, and conventional cloud-removal pipel…
  datacite Zhang, Bohan, Xu, Chenyu, Mao, Yijie, Shi, Yuanming 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Artificial Intelligence (cs.AI)Computer Vision and Pattern Recognition (cs.CV)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• Artifacts for "Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis"
  This repository contains the artifacts associated with the paper"Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis" (Duque, Hendrikx, Gorce - 2026). Contents - federated_csinet_dataset.mat: QuaDRiGa-generated dataset …
  datacite Duqué, Loukas, Gorce, Jean-Marie, Hendrikx, Hadrien 2026 置信度 0.66
• Artifacts for "Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis"
  This repository contains the artifacts associated with the paper"Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis" (Duque, Hendrikx, Gorce - 2026). Contents - federated_csinet_dataset.mat: QuaDRiGa-generated dataset …
  datacite Duqué, Loukas, Gorce, Jean-Marie, Hendrikx, Hadrien 2026 置信度 0.66
• Blockchain Solution with Artificial Intelligence Integration in the Indian Judicial System A Bibliometric and Methodical Literature Review
  The Indian judicial system faces critical challenges including case backlogs exceeding 54.7 million pending matters, insufficient judicial resources, and inefficient evidence management processes. This literature review examines the emerging potential of integ…
  datacite Sameer Patil, Darshana Desai 2026 置信度 0.66 BlockchainArtificial IntelligenceJudicial SystemLegal TechnologyIndia
• Blockchain Solution with Artificial Intelligence Integration in the Indian Judicial System A Bibliometric and Methodical Literature Review
  The Indian judicial system faces critical challenges including case backlogs exceeding 54.7 million pending matters, insufficient judicial resources, and inefficient evidence management processes. This literature review examines the emerging potential of integ…
  datacite Sameer Patil, Darshana Desai 2026 置信度 0.66 BlockchainArtificial IntelligenceJudicial SystemLegal TechnologyIndia
• 7th International Conference on Machine Learning & Trends (MLT 2026)
  7th International Conference on Machine Learning & Trends (MLT 2026) June 20 ~ 21, 2026, Sydney, Australia https://sai2026.org/mlt/index Scope & Topics 7th International Conference on Machine Learning & Trends (MLT 2026) serves as a premier global forum for pr…
  datacite ''Flores Kú'', José Martin 2026 置信度 0.66
• 7th International Conference on Machine Learning & Trends (MLT 2026)
  7th International Conference on Machine Learning & Trends (MLT 2026) June 20 ~ 21, 2026, Sydney, Australia https://sai2026.org/mlt/index Scope & Topics 7th International Conference on Machine Learning & Trends (MLT 2026) serves as a premier global forum for pr…
  datacite ''Flores Kú'', José Martin 2026 置信度 0.66
• Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions
  Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central server. However, existing DFL solutions do not guarantee global model consistency, a critical requirement for co…
  datacite Li, Yue, Bhujel, Sudip, Lira, Cameron, Wang, Ning 等 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciences
• Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem
  Federated Continual Learning (FCL) is fundamental to real-world distributed learning systems, requiring models to adapt to sequential, non-IID data across clients while mitigating catastrophic forgetting and client drift. Existing approaches formulate continua…
  datacite Shah, Nazreen, Somireddy, Naveen Kumar Reddy, Shaban, Zubair, Prasad, Ranjitha 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• CONFERENCE Proceedings: 1 INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND BUSINESS DIGITAL TRANSFORMATION (ICETBDT 2026) - GOA, INDIA
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  datacite Ferlic, Randolph James, Ferlic, Kimberly Kate 2026 置信度 0.66 privacy-preserving computationanonymizationcross-subject aggregationfederated learningByzantine-robust aggregation
• Privacy-Preserving Operations on Spiral-Domain Encoded Time-Series States: Anonymization, Aggregation, and Federated Learning with Composition Rules and Streaming Variants
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  datacite Murtaza Rangwala 2026 置信度 0.66
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