arXiv Machine Learning By Luca Barbieri, Gianluca Fontanesi, Lorenzo Galati Giordano, Alfonso Fernandez Duran, Thorsten Wild

An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks

Read the original on arXiv Machine Learning →

arXiv:2607. 26682v1 Announce Type: cross Abstract: Centrally-managed Wi-Fi solutions are increasingly leveraging Distributed Artificial Intelligence (AI) to predict key operational statistics of Access Points (APs) and proactively optimize network performance.

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arXiv Machine Learning
Aug 4

Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.

By Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman
arXiv Machine Learning
Jul 21

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).

By Ferdous Pervej, Minseok Choi, Andreas F. Molisch