Synergies Between Federated Learning and O-RAN: Towards an Elastic Architecture for Multiple Distributed Machine Learning Services

• URL: http://arxiv.org/abs/2305.02109v4
• Date: Mon, 02 Dec 2024 21:10:41 GMT
• Title: Synergies Between Federated Learning and O-RAN: Towards an Elastic Architecture for Multiple Distributed Machine Learning Services
• Authors: Payam Abdisarabshali, Nicholas Accurso, Filippo Malandra, Weifeng Su, Seyyedali Hosseinalipour,
• Abstract summary: Federated learning (FL) over 5G-and-beyond wireless networks is a popular distributed machine learning (ML) technique. implementation of FL over 5G-and-beyond wireless networks faces key challenges caused by (i) dynamics of the wireless network conditions and (ii) the coexistence of multiple FL-services in the system. We first take a closer look into these challenges and unveil nuanced phenomena called over-/under-provisioning of resources and perspective-driven load balancing. We then take the first steps towards addressing these phenomena by proposing a novel distributed ML architecture called elastic FL (EFL)
• Score: 7.057114677579558
• License:
• Abstract: Federated learning (FL) is a popular distributed machine learning (ML) technique. However, implementation of FL over 5G-and-beyond wireless networks faces key challenges caused by (i) dynamics of the wireless network conditions and (ii) the coexistence of multiple FL-services in the system, which are not jointly considered in prior works. We first take a closer look into these challenges and unveil nuanced phenomena called over-/under-provisioning of resources and perspective-driven load balancing. We then take the first steps towards addressing these phenomena by proposing a novel distributed ML architecture called elastic FL (EFL). EFL unleashes the full potential of Open RAN (O-RAN) systems and introduces an elastic resource provisioning methodology to execute FL-services. It further constitutes a multi-time-scale FL management system that introduces three dedicated network control functionalities tailored for FL-services, including (i) non-real-time (non-RT) system descriptor, which trains ML-based applications to predicted both system and FL-related dynamics and parameters; (ii) near-RT FL controller, which handles O-RAN slicing and mobility management for the seamless execution of FL-services; (iii) FL MAC scheduler, which conducts real-time resource allocation to the end clients of various FL-services. We finally prototype EFL to demonstrate its potential in improving the performance of FL-services.

Related papers

• Federated Learning in Practice: Reflections and Projections [17.445826363802997]
  Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Production systems from organizations like Google, Apple, and Meta demonstrate the real-world applicability of FL. We propose a redefined FL framework that prioritizes privacy principles rather than rigid definitions.
  arXiv  Detail & Related papers  (2024-10-11T15:10:38Z)
• FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments [2.553456266022126]
  This paper presents Federated Learning with Adaptive Monitoring and Elimination (FLAME) FLAME is a novel solution capable of detecting and mitigating concept drift in Federated Learning (FL) Internet of Things (IoT) environments.
  arXiv  Detail & Related papers  (2024-10-02T09:55:58Z)
• FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA [0.0]
  Federated Learning (FL) has emerged as a promising approach for privacy preservation. This article investigates the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss.
  arXiv  Detail & Related papers  (2023-03-29T16:36:42Z)
• FS-Real: Towards Real-World Cross-Device Federated Learning [60.91678132132229]
  Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data. There is still a considerable gap between the flourishing FL research and real-world scenarios, mainly caused by the characteristics of heterogeneous devices and its scales. We propose an efficient and scalable prototyping system for real-world cross-device FL, FS-Real.
  arXiv  Detail & Related papers  (2023-03-23T15:37:17Z)
• Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence [83.58839320635956]
  Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recent FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets. This paper addresses how much benefit MAML brings to FL and how to maximize such benefit over mobile edge networks.
  arXiv  Detail & Related papers  (2023-03-23T02:42:10Z)
• Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks [56.91063444859008]
  Federated Learning (FL) is a collaborative machine learning framework that combines on-device training and server-based aggregation. We propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. We show that an age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.
  arXiv  Detail & Related papers  (2022-12-14T17:33:01Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks [56.68149211499535]
  Federated learning (FL) is a key enabler for efficient communication and computing leveraging devices' distributed computing capabilities. This paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNNs) We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2022-03-26T15:06:13Z)
• Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks [52.93232352968347]
  This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) FL preserves data privacy by exchanging the locally trained models of mobile devices. SNNs are however non-trivial, particularly under wireless connections with time-varying channel conditions. We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2021-12-05T11:17:17Z)
• How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups? [42.63398054091038]
  We propose a cell-free massive multiple-input multiple-output (MIMO) network to guarantee the stable operation of multiple FL processes. We then develop a novel scheme that asynchronously executes the iterations of FL processes under multicasting downlink and conventional uplink transmission protocols.
  arXiv  Detail & Related papers  (2021-07-20T15:46:53Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.