Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Training ML models which are fair across different demographic groups is of critical
importance due to the increased integration of ML in crucial decision-making scenarios such …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

Federated Learning (FL) bridges the gap between collaborative machine learning and
preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is …

The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …

In collaborative machine learning (CML), multiple agents pool their resources (eg, data)
together for a common learning task. In realistic CML settings where the agents are self …