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Found 7 papers, 0 papers with code
Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks
Based on this bound, we minimize a weighted utility function for jointly configuring the controllable parameters to train the RawHFL energy efficiently under practical resource constraints.
Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks
Video caching can significantly improve backhaul traffic congestion by locally storing the popular content that users frequently request.
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity
While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth.
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles
This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model.
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold.
Vehicular Network Slicing for Reliable Access and Deadline-Constrained Data Offloading: A Multi-Agent On-Device Learning Approach
To that end, considering practical location-aware node associations, a joint radio and power resource allocation non-cooperative stochastic game is formulated.
Networking and Internet Architecture Distributed, Parallel, and Cluster Computing Multiagent Systems Systems and Control Signal Processing Systems and Control
Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks
Unlike these legacy modeling paradigms, this paper considers heterogeneous content preference of the users with heterogeneous caching models at the edge nodes.
