MUFFLe: Efficient Model Update Compression via Generalized Deduplication for Federated Learning

Abstract:Federated learning is well suited to edge environments but is often limited by the uplink cost of transmitting model updates. This Work-in-Progress paper presents MUFFLe, a communication-efficient update compression scheme that integrates generalized deduplication (GD) into the FedAvg pipeline. MUFFLe deduplicates repeated patterns across the update vector, yielding a fixed-rate, variable-count compression scheme. Preliminary experiments on IID MNIST with 20 clients show that MUFFLe reaches the target accuracy of $92.93\%$ with 38~MB cumulative uplink communication, compared with 75~MB for 8-bit quantization, 86~MB for Top-$k$ sparsification, and 310~MB for uncompressed FedAvg. These results demonstrate the feasibility of applying GD to communication-efficient federated learning.

Machine Learning (cs.LG)

arXiv:2606.14354 [cs.LG]

(or arXiv:2606.14354v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.14354 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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