Igor Čavrak

FEDCODE: A Framework and Experimental Study of Model-Update Compression for Communication-Efficient Federated Learning

Federated learning trains shared models without centralizing local data, but repeated model exchange can become a bottleneck in bandwidth-, energy-, and latency-constrained edge systems. This paper presents FEDCODE, a communication-aware federated learning simulation framework for reproducible evaluation of update repr...