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Found 6 papers, 1 papers with code
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system.
Mode Connectivity and Data Heterogeneity of Federated Learning
Empirically, reducing data heterogeneity makes the connectivity on different paths more similar, forming more low-error overlaps between client and global modes.
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality
In this paper, we tackle this desynchronization using a continual RL framework that facilitates the resource allocation dynamically for MEC-enabled VR content streaming.
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Based on these findings from our loss landscape visualization and loss decomposition, we propose utilizing iterative moving averaging (IMA) on the global model at the late training phase to reduce its deviation from the expected minimum, while constraining client exploration to limit the maximum distance between the global and client models.
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
This enables client models to be updated in a shared feature space with consistent classifiers during local training.
