A Fast Blockchain-based Federated Learning Framework with Compressed Communications. (arXiv:2208.06095v1 [cs.LG])

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). Nevertheless, BFL tremendously
escalates the communication traffic volume because all local model updates
(i.e., changes of model parameters) obtained by BFL clients will be transmitted
to all miners for verification and to all clients for aggregation. In contrast,
the parameter server and clients …

blockchain communications fast federated learning framework lg