A multifaceted survey on federated learning: Fundamentals, paradigm shifts, practical issues, recent developments, partnerships, trade-offs, trustworthiness, and ways …
A Majeed, SO Hwang - IEEE Access, 2024 - ieeexplore.ieee.org
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 …
environments because it does not require data to be aggregated in some central place to …
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Federated learning (FL) is an emerging distributed machine learning paradigm that enables
collaborative training of machine learning models over decentralized devices without …
collaborative training of machine learning models over decentralized devices without …
Incentive-Compatible Federated Learning with Stackelberg Game Modeling
Federated Learning (FL) has gained prominence as a decentralized machine learning
paradigm, allowing clients to collaboratively train a global model while preserving data …
paradigm, allowing clients to collaboratively train a global model while preserving data …
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Federated learning (FL) is an emerging distributed machine learning paradigm enabling
collaborative model training on decentralized devices without exposing their local data. A …
collaborative model training on decentralized devices without exposing their local data. A …