A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often suffers from significant communication and computational overhead, limiting its scalability and sustainability. In this work, we introduce a Full Compression Pipeline (FCP) for FL in communication-constrained environments. FCP integrates three complementary deep compression techniques (pruning, quantization, and Huffman encoding) into a unified end-to-end framework. By compressing local models and communication payloads, FCP substantially reduces transmission costs and resource consumption while maintaining competitive accuracy. To...
| Authors | Elouan Colybes, Shirin Salehi, Anke Schmeink |
|---|---|
| Primary category | cs.LG |
| Published | 13 April 2026 |
| Last updated at source | 14 April 2026 |
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