1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
A Fast Blockchain-based Federated Learning Framework with Compressed Communications
Laizhong Cui, Xiaoxin Su, Yipeng Zhou
Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoi…
Optimal Rate Adaption in Federated Learning with Compressed Communications
Laizhong Cui, Xiaoxin Su, Yipeng Zhou +1
Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates. Yet the tradeoff between compression and model accurac…
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…