analiza aiqbench
FedGuide advances federated reinforcement learning by leveraging diffusion priors and optimal transport to handle distributional mismatches across heterogeneous clients without direct policy averaging. This approach preserves diverse behavior modes while introducing a low-variance value baseline to improve robustness in non-IID environments. The method represents a significant step toward scalable collaborative AI systems operating under realistic data heterogeneity constraints.
FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning
arXiv:2609.18964v1 Announce Type: cross Abstract: Federated Reinforcement Learning (FRL) enables collaborative policy learning across distributed agents with heterogeneous environments. While recent methods based on variance reduction, divergence penalization, and momentum optimization improve FRL under heterogeneous settings, they still primarily synchronize policy or value-network parameters and do not explicitly address distributional mismatch among heterogeneous clients. Therefore, we propose \textbf{FedGuide}, a FRL framework that uses diffusion priors as behavior models to provide personalized data supported distributions for heterogeneous local policy learning. Instead of directly averaging local policies, FedGuide aggregates those diffusion priors through Optimal-Transport Mixture-of-Experts (OT-MoE), preserving heterogeneous behavior modes in distribution space. It further develops a Distribution Correction Estimation (DICE) value baseline to provide low-variance, return-aware guidance for local policy improvement. Experiments across heterogeneous environments show that FedGuide outperforms representative FRL methods in client-average returns, final-round performance, and worst-round robustness, while maintaining stable learning under stronger heterogeneity.
Abstract:Federated Reinforcement Learning (FRL) enables collaborative policy learning across distributed agents with heterogeneous environments. While recent methods based on variance reduction, divergence penalization, and momentum optimization improve FRL under heterogeneous settings, they still primarily synchronize policy or value-network parameters and do not explicitly address distributional mismatch among heterogeneous clients. Therefore, we propose \textbf{FedGuide}, a FRL framework that uses diffusion priors as behavior models to provide personalized data supported distributions for heterogeneous local policy learning. Instead of directly averaging local policies, FedGuide aggregates those diffusion priors through Optimal-Transport Mixture-of-Experts (OT-MoE), preserving heterogeneous behavior modes in distribution space. It further develops a Distribution Correction Estimation (DICE) value baseline to provide low-variance, return-aware guidance for local policy improvement. Experiments across heterogeneous environments show that FedGuide outperforms representative FRL methods in client-average returns, final-round performance, and worst-round robustness, while maintaining stable learning under stronger heterogeneity.
Machine Learning (cs.LG); Robotics (cs.RO)
arXiv:2609.18964 [cs.LG]
(or arXiv:2609.18964v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.18964 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 237 cuvinte
Subiecte conexe
The Continual Constitutional Convention: How language models revise the documents that govern them
AI Alignment Forum · 13 oct. 2026
V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
arXiv · 2 sept. 2026
InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation
arXiv · 3 sept. 2026
NoiseTilt: Noise-Tilted Reverse Kernels for Diffusion Reward Alignment
arXiv · 17 iun. 2026