Federated Learning in MIMO Satellite Broadcast System
This addresses privacy and efficiency issues for wireless edge systems, but appears incremental as it builds on existing federated learning approaches.
The paper tackles the problem of privacy vulnerabilities and accuracy degradation in existing federated learning methods by incorporating federated learning into MIMO satellite broadcast systems, but no concrete results or numbers are provided.
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.