Summary of Version Age-based Client Scheduling Policy For Federated Learning, by Xinyi Hu et al.
Version age-based client scheduling policy for federated learningby Xinyi Hu, Nikolaos Pappas, Howard H. YangFirst…
Version age-based client scheduling policy for federated learningby Xinyi Hu, Nikolaos Pappas, Howard H. YangFirst…
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detectionby Pramit…
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learningby Meiying Zhang, Huan Zhao,…
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codesby Xiaoxin Su, Yipeng Zhou, Laizhong Cui, John…
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compressionby Xiaoxin Su, Yipeng Zhou, Laizhong Cui,…
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guaranteesby Shahryar Zehtabi, Dong-Jun Han,…
Fairness and Privacy Guarantees in Federated Contextual Banditsby Sambhav Solanki, Shweta Jain, Sujit GujarFirst submitted…
Federated Learning with Differential Privacyby Adrien Banse, Jan Kreischer, Xavier Oliva i JürgensFirst submitted to…
Federated Learning with New Knowledge: Fundamentals, Advances, and Futuresby Lixu Wang, Yang Zhao, Jiahua Dong,…
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasksby Yun-Wei Chu, Dong-Jun Han, Seyyedali…