arXiv AI
Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning
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Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning
arXiv:2607. 13119v1 Announce Type: cross Abstract: In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration.
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.
arXiv Machine Learning
Jun 9
Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design
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arXiv Machine Learning
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QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles
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arXiv AI
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AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling
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arXiv Machine Learning
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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).