In federated learning (FL), multiple clients collaborate to train machine learning models
together while keeping their data decentralized. Through utilizing more training data, FL …

In Federated Learning (FL), the distributed nature and heterogeneity of client data present
both opportunities and challenges. While collaboration among clients can significantly …

Non-IID data present a tough challenge for federated learning. In this paper, we explore a
novel idea of facilitating pairwise collaborations between clients with similar data. We …

Federated learning (FL) is an emerging technology that enables the training of machine
learning models from multiple clients while keeping the data distributed and private. Based …

Knowledge sharing and model personalization are essential components to tackle the non-
IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) …

FedLab is a lightweight open-source framework for the simulation of federated learning. The
design of FedLab focuses on federated learning algorithm effectiveness and communication …

Hierarchical federated learning (HFL) allows multiple edge aggregations at edge devices
before one global aggregation to address both issues of non-independent and identically …

Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …

Federated Learning (FL) has emerged as a promising distributed learning paradigm that
enables multiple clients to learn a global model collaboratively without sharing their private …

Federated learning has attracted increasing attention with the emergence of distributed data.
While extensive federated learning algorithms have been proposed for the non-convex …