Over-the-Air Federated Learning In Broadband Communication

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that operates at the wireless edge. It enables clients to collaborate on model training while keeping their data private from adversaries and the central server. However, current FL approaches have limitations. Some rely on secure multiparty computation, which can be vulnerable to inference attacks. Others employ differential privacy, but this may lead to decreased test accuracy when dealing with a large number of parties contributing small amounts of data. To address these issues, this paper proposes a novel approach that integrates federated learning seamlessly into the inner workings of MIMO (Multiple-Input Multiple-Output) systems.

Federated Learning in MIMO Satellite Broadcast System

Federated learning (FL) is a type of distributed machine learning at the wireless edge that preserves the privacy of clients' data from adversaries and even the central server. Existing federated learning approaches either use (i) secure multiparty computation (SMC) which is vulnerable to inference or (ii) differential privacy which may decrease the test accuracy given a large number of parties with relatively small amounts of data each. To tackle the problem with the existing methods in the literature, In this paper, we introduce incorporate federated learning in the inner-working of MIMO systems.