Related papers: FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems

FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems

URL: http://arxiv.org/abs/2506.02668v1
Date: Tue, 03 Jun 2025 09:15:03 GMT
Title: FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems
Authors: Frederico Metelo, Alexandre Oliveira, Stevo Racković, Pedro Ákos Costa, Cláudia Soares,
Abstract summary: We present textbfFAuNO -- emphFederated Asynchronous Network Orchestrator -- a buffered, asynchronous emphfederated reinforcement-learning framework for decentralized task offloading in edge systems.<n>Experiments in the emphPeersimGym environment show that FAuNO consistently matches or exceeds federated multi-agent RL baselines in reducing task loss and latency.
Score: 41.364418162255184
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: 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 \textbf{FAuNO} -- \emph{Federated Asynchronous Network Orchestrator} -- a buffered, asynchronous \emph{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 \emph{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.

Related papers

A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
arXiv Detail & Related papers (2026-02-23T16:12:02Z)
HALO: Semantic-Aware Distributed LLM Inference in Lossy Edge Network [50.33808558714122]
Large language models' (LLMs) inference at the edge can facilitate prompt service responsiveness while protecting user privacy.<n>We propose HALO, a novel framework that can boost the distributed LLM inference in lossy edge network.<n> Experimental results from a Raspberry Pi cluster demonstrate that HALO achieves a 3.41x end-to-end speedup for LLaMA-series LLMs under unreliable network conditions.
arXiv Detail & Related papers (2026-01-16T07:37:23Z)
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning [1.1510009152620668]
asynchronous learning (FL) enables collaborative model training across distributed clients while preserving data locality.<n>We introduce Fast-and-Reliable AI Network, FRAIN, a new FL method that mitigates these limitations by incorporating two key ideas.<n>Experiments with a CNN image-classification model and a Transformer-based language model demonstrate that FRAIN achieves more stable and robust convergence than FedAvg, FedAsync, and BRAIN.
arXiv Detail & Related papers (2025-05-07T08:20:23Z)
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
arXiv Detail & Related papers (2024-10-16T16:13:19Z)
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
arXiv Detail & Related papers (2024-03-27T09:14:36Z)
On the Role of Server Momentum in Federated Learning [85.54616432098706]
We propose a general framework for server momentum, that (a) covers a large class of momentum schemes that are unexplored in federated learning (FL) We provide rigorous convergence analysis for the proposed framework.
arXiv Detail & Related papers (2023-12-19T23:56:49Z)
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation. We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
arXiv Detail & Related papers (2023-11-03T13:34:44Z)
FAVANO: Federated AVeraging with Asynchronous NOdes [14.412305295989444]
We propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO, for training Deep Neural Networks (DNNs) in resource constrained environments.
arXiv Detail & Related papers (2023-05-25T14:30:17Z)
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control [37.54493447920386]
We propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme to meet asymmetric and heterogeneous traffic demands. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm.
arXiv Detail & Related papers (2022-11-04T07:39:21Z)
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks [88.50555581186799]
We propose a semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized FederatedAveraging (PerFedS$2$), over mobile edge networks. We derive an upper bound of the convergence rate of PerFedS2 in terms of the number of participants per global round and the number of rounds. Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss.
arXiv Detail & Related papers (2022-09-27T02:12:43Z)
Asynchronous Hierarchical Federated Learning [10.332084068006345]
Asynchronous hierarchical federated learning is proposed to solve problems of heavy server traffic, long periods of convergence, and unreliable accuracy. A special aggregator device is selected to enable hierarchical learning, so that the burden of the server can be significantly reduced. We evaluate the proposed algorithm on CIFAR-10 image classification task.
arXiv Detail & Related papers (2022-05-31T18:42:29Z)
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients [3.983055670167878]
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-decentralized FEEL (SD-FEEL) architecture where multiple edge servers collaborate to incorporate more data from edge devices in training.
arXiv Detail & Related papers (2021-12-09T07:39:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.