mlsanityData to 14 Oct 2026

Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears

arXiv 2508.17262 ↗First posted 24 Aug 2025cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Extended reality technologies are transforming fields such as healthcare, entertainment, and education, with Smart Eye-Wears (SEWs) and Artificial Intelligence (AI) playing a crucial role. However, SEWs face inherent limitations in computational power, memory, and battery life, while offloading computations to external servers is constrained by network conditions and server workload variability. To address these challenges, we propose a Federated Reinforcement Learning (FRL) framework, enabling multiple agents to train collaboratively while preserving data privacy. We implemented synchronous and asynchronous federation strategies, where models are aggregated either at fixed intervals or dynamically based on agent progress. Experimental results show that federated agents exhibit significantly lower performance variability, ensuring greater stability and reliability. These findings underscore the potential of FRL for applications requiring robust real-time AI processing, such as real-time object detection in SEWs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 30 references that are in the map. First posted on arXiv 24 Aug 2025.

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6 of 30 references.

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  1. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach11 Jun

Other references · 5

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2014Multiple Object Tracking: A Literature Reviewcited by 166
  3. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  4. 2023Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learningcited by 3
  5. 2022Partitioning and Placement of Deep Neural Networks on Distributed Edge Devices to Maximize Inference Throughputcited by 1

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  1. 2021Federated Ensemble Model-based Reinforcement Learning in Edge Computing
  2. 2026Agentic Federated Learning: The Future of Distributed Training Orchestration
  3. 2025Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
  4. 2025Optimized Local Updates in Federated Learning via Reinforcement Learning
  5. 2025FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems
  6. 2025Federated Reinforcement Learning in Heterogeneous Environments
  7. 2026FederatedSkill: Federated Learning for Agentic Skill Evolution
  8. 2024Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments