The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

Recently emerged federated learning (FL) is an attractive distributed learning framework in
which numerous wireless end-user devices can train a global model with the data remained …

Byzantine-robust federated learning aims to enable a service provider to learn an accurate
global model when a bounded number of clients are malicious. The key idea of existing …

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in
the form of backdoors during training. The goal of a backdoor is to corrupt the performance …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Federated learning over distributed multi-party data is an emerging paradigm that iteratively
aggregates updates from a group of devices to train a globally shared model. Relying on a …

Federated Learning is the current state-of-the-art in supporting secure multi-party machine
learning (ML): data is maintained on the owner's device and the updates to the model are …

By exploiting the computing power and local data of distributed clients, federated learning
(FL) features ubiquitous properties such as reduction of communication overhead and …

Existing model poisoning attacks to federated learning assume that an attacker has access
to a large fraction of compromised genuine clients. However, such assumption is not realistic …

While federated learning (FL) allows efficient model training with local data at edge devices,
among major issues still to be resolved are: slow devices known as stragglers and malicious …