QuPeL: Quantized Personalization with Applications to Federated Learning

Understand

Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server.

• Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients with {\em diverse resources}.
• In this work, we introduce a \textit{quantized} and \textit{personalized} FL algorithm QuPeL that facilitates collective training with heterogeneous clients while respecting resource diversity.
• For personalization, we allow clients to learn \textit{compressed personalized models} with different quantization parameters depending on their resources.