Integrated sensing, lighting and communication based on visible light communication: A review
As wireless communication rapidly evolves and the demand for intelligent connectivity
grows, the need for precise sensing integrated with efficient communication becomes …
grows, the need for precise sensing integrated with efficient communication becomes …
Limitations and future aspects of communication costs in federated learning: A survey
This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …
modern distributed systems. FL is an emerging distributed machine learning technique that …
Communication resources constrained hierarchical federated learning for end-to-end autonomous driving
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 …
by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence …
[HTML][HTML] Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework
The 5 G networks are effectively deployed worldwide, and academia and industries have
begun looking at 6 G network communication technology for consumer electronics …
begun looking at 6 G network communication technology for consumer electronics …
Joint age-based client selection and resource allocation for communication-efficient federated learning over noma networks
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 …
while retaining their own training data locally. Nevertheless, the performance of FL is often …
Distributed linear bandits under communication constraints
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 …
the overall cumulative regret incurred by all agents. Information exchange is facilitated by a …
[HTML][HTML] A multi-objective approach for communication reduction in federated learning under devices heterogeneity constraints
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 …
models instead of raw data during each iteration of model training. However, these models …
Energy-Efficient Connectivity-Aware Learning Over Time-Varying D2D Networks
Semi-decentralized federated learning blends the conventional device-to-server (D2S)
interaction structure of federated model training with localized device-to-device (D2D) …
interaction structure of federated model training with localized device-to-device (D2D) …
Sparse training for federated learning with regularized error correction
R Greidi, K Cohen - IEEE Journal of Selected Topics in Signal …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) is an emerging paradigm that allows for decentralized machine
learning (ML), where multiple models are collaboratively trained in a privacy-preserving …
learning (ML), where multiple models are collaboratively trained in a privacy-preserving …
Federated learning for 6g: Paradigms, taxonomy, recent advances and insights
Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …