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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy.
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients.
A Fast Blockchain-based Federated Learning Framework with Compressed Communications
Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL).
Optimal Rate Adaption in Federated Learning with Compressed Communications
Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates.
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster.
