← Search

Francesco Pase

2 accepted papers

2024

Adaptive Compression in Federated Learning via Side Information

AISTATS 2024poster

The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL). Among existing approaches, state-of-the-art bitrate-accuracy tradeoffs have been achieved using stochastic compression methods – in which the client n…

2023

Sparse Random Networks for Communication-Efficient Federated Learning

ICLR 2023poster

One main challenge in federated learning is the large communication cost of exchanging weight updates from clients to the server at each round. While prior work has made great progress in compressing the weight updates through gradient compression methods, we propose a radically different approach t…