arXiv:2605.29002v1 Announce Type: new Abstract: Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories. However, FedAvg-style parameter averaging is not function-space consistent: when clients use heterogeneous encoders or even identical nonlinear networks, averaged parameters need not correspond to the weighted average of client value functions in any common function space. We propose FedQHD, a federated Q-learning method using hyperdimensional (random-feature) state encoders with a linear readou
The increasing complexity of AI systems and the growing need for privacy-preserving, decentralized learning necessitate new architectures in federated reinforcement learning.
This development addresses critical challenges in federated AI, enabling more robust and consistent policy learning across distributed agents, which is crucial for scalable and private AI applications.
Traditional federated averaging methods in reinforcement learning are becoming function-space consistent, allowing for more reliable collaboration among heterogeneous AI agents.
- · Decentralized AI development
- · Privacy-preserving AI applications
- · Robotics and autonomous systems
- · AI research and development
- · Centralized AI training paradigms
- · Methods reliant on raw data exchange
- · AI systems unable to adapt to heterogeneity
Improved performance and scaling of federated reinforcement learning systems across diverse environments and agents.
Accelerated adoption of federated learning in sectors like robotics, IoT, and healthcare due to enhanced reliability and privacy.
The development of entirely new classes of AI agents capable of continuous, collaborative learning without exposing sensitive data.
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