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. FL is appealing in preserving data privacy; yet the communication between the PS and scattered clients can be a severe bottleneck. Model compression algorithms, such as quantization and sparsification, have been suggested but they generally assume a fixed code length, which does not reflect the heterogeneity …

aggregation collection communication communications cs.lg data data privacy distributed distribution fed federated learning multiple paradigm privacy private data server update