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cs.LG2024
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Xiaoxin Su, Yipeng Zhou, Laizhong Cui +1
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates…
cs.LG2024
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
Xiaoxin Su, Yipeng Zhou, Laizhong Cui +2
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model di…
cs.LG2021
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
Laizhong Cui, Xiaoxin Su, Yipeng Zhou +1
Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that im…