I. Introduction
Federated Learning (FL) is a new Machine Learning (ML) paradigm recently gaining significant interest from academia and industry. It offers learning from distributed clients without sharing data, thus assuring a certain level of privacy. Aside from providing data privacy, FL also facilitates access to heterogeneous data, improving the learned models' overall accuracy. In an FL system, each client trains a local model using its data and exchanges only the model parameters with a centralized FL server (also called an aggregator) for aggregation. The server periodically merges clients' local models by taking their average to generate a new global model shared with clients and used in subsequent training rounds [1]. Thus, the server is a central player that potentially represents a single point of failure [2]. In addition, FL systems are subject to malicious clients trying to harm the global model performance by submitting falsified local model updates.