Federated Learning (FL) seeks to train a model collaboratively without sharing private
training data from individual clients. Despite its promise, FL encounters challenges such as …

The emerging Personalized Federated Learning (PFL) methods aim to produce
personalized models for different users, so as to keep track of their individualized …

Recently, Federated Learning (FL) has been applied in various research domains specially
because of its privacy preserving and decentralized approach of model training. However …

This paper proposes a client selection (CS) method to tackle the communication bottleneck
of federated learning (FL) while concurrently coping with FL's data heterogeneity issue …

Federated learning enables distributed machine learning for decentralized data on edge
devices. As communication is a critical bottleneck for federated learning, we utilize model …

Federated learning (FL) is a distributed machine learning paradigm that needs collaboration
between a server and a series of clients with decentralized data. To make FL effective in real …

This paper presents a federated learning approach based on utilizing computational
resources of the IoT edge devices for training deep neural networks. In this approach, the …

Federated distillation, a new algorithmic paradigm in Federated learning, enables clients to
train different network architectures. In federated distillation, students can learn information …

Federated Learning (FL) promises to solve the data privacy problem by training the local
model on each node and sharing the model parameters instead of the data itself. Next, the …

Federated learning (FL) is a novel learning paradigm that addresses the privacy leakage
challenge of centralized learning. However, in FL, users with non-independent and …