As wireless communication rapidly evolves and the demand for intelligent connectivity
grows, the need for precise sensing integrated with efficient communication becomes …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

While federated learning (FL) improves the generalization of end-to-end autonomous driving
by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence …

The 5 G networks are effectively deployed worldwide, and academia and industries have
begun looking at 6 G network communication technology for consumer electronics …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

In federated learning (FL), distributed clients can collaboratively train a shared global model
while retaining their own training data locally. Nevertheless, the performance of FL is often …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

We consider distributed linear bandits where $ M $ agents learn collaboratively to minimize
the overall cumulative regret incurred by all agents. Information exchange is facilitated by a …

Federated learning is a paradigm that proposes protecting data privacy by sharing local
models instead of raw data during each iteration of model training. However, these models …

Semi-decentralized federated learning blends the conventional device-to-server (D2S)
interaction structure of federated model training with localized device-to-device (D2D) …