학술논문

Federated Learning in MIMO Satellite Broadcast System
Document Type
Working Paper
Source
Subject
Electrical Engineering and Systems Science - Signal Processing
Computer Science - Machine Learning
Language
Abstract
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.