FedQHD achieves closed-form federated Q-learning via hyperdimensional encoders with linear readouts, formalizes the federation gap under heterogeneous encoders, and reports competitive performance on continuous-state benchmarks with reduced computation.
Boyi Liu, Lujia Wang, and Ming Liu
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HASA computes client heterogeneity scores from local data and assigns wider subnets to less heterogeneous clients, raising mean client test accuracy from 13.82% to 14.32% and improving worst-client accuracy versus uniform and partial-training baselines under matched compute budgets on a seven-client
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FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
FedQHD achieves closed-form federated Q-learning via hyperdimensional encoders with linear readouts, formalizes the federation gap under heterogeneous encoders, and reports competitive performance on continuous-state benchmarks with reduced computation.
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HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
HASA computes client heterogeneity scores from local data and assigns wider subnets to less heterogeneous clients, raising mean client test accuracy from 13.82% to 14.32% and improving worst-client accuracy versus uniform and partial-training baselines under matched compute budgets on a seven-client