Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics

Understand

Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data.

• It consists of a parameter server and a possibly large collection of clients (e.g., in cross-device federated learning) that may operate in congested and changing environments.
• In this paper, we study federated learning in the presence of stochastic and dynamic communication failures wherein the uplink between the parameter server and client $i$ is on with unknown probability $p_i^t$ in round $t$.
• Furthermore, we allow the dynamics of $p_i^t$ to be arbitrary.