FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Edge computing addresses the growing data demands of connected-device networks by placing computational resources closer to end users through decentralized infrastructures. This decentralization challenges traditional, fully centralized orchestration, which suffers from latency and resource bottlenecks. We present FAuNO -- Federated Asynchronous Network Orchestrator -- a buffered, asynchronous federated reinforcement-learning (FRL) framework for decentralized task offloading in edge systems. FAuNO adopts an actor-critic architecture in which local actors learn node-specific dynamics and peer interactions, while a federated critic aggregates experience across agents to encourage efficient cooperation and improve overall system performance. Experiments in the PeersimGym environment show that FAuNO consistently matches or exceeds heuristic and federated multi-agent RL baselines in reducing task loss and latency, underscoring its adaptability to dynamic edge-computing scenarios.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 45 references that are in the map. First posted on arXiv 3 Jun 2025.
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19 of 45 references.
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- 2024PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning26 Mar
Other references · 18
- 2017Proximal Policy Optimization Algorithmscited by 14,592
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
- 2018Federated Optimization in Heterogeneous Networkscited by 2,981
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- 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
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- 2020When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Networkcited by 30
- 2020Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systemscited by 20
6 more
- 2017Learning-Based Computation Offloading for IoT Devices with Energy Harvestingcited by 15
- 2017Next Generation Cloud Computing: New Trends and Research Directionscited by 8
- 2024Measuring Policy Distance for Multi-Agent Reinforcement Learningcited by 4
- 2019Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithmcited by 3
- 2020Heterogeneous Task Offloading and Resource Allocations via Deep Recurrent Reinforcement Learning in Partial Observable Multi-Fog Networkscited by 3
- 2022A Review of Resource Management in Fog Computing: Machine Learning Perspectivecited by 1
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