In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …

The emerging paradigm of federated learning (FL) strives to enable collaborative training of
deep models on the network edge without centrally aggregating raw data and hence …

A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

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 devices collaboratively
train a machine learning model while keeping the training data decentralized. In most of the …

Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …