Blockchain-enabled Server-less Federated Learning

• URL: http://arxiv.org/abs/2112.07938v1
• Date: Wed, 15 Dec 2021 07:41:23 GMT
• Title: Blockchain-enabled Server-less Federated Learning
• Authors: Francesc Wilhelmi, Lorenza Giupponi, Paolo Dini
• Abstract summary: We focus on an asynchronous server-less Federated Learning solution empowered by (BC) technology. In contrast to mostly adopted FL approaches, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates.
• Score: 5.065631761462706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by the heterogeneous nature of devices participating in large-scale Federated Learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by Blockchain (BC) technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting fits well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efficiency in terms of communication overhead and idle periods. To evaluate the learning completion delay of BC-enabled FL, we provide an analytical model based on batch service queue theory. Furthermore, we provide simulation results to assess the performance of both synchronous and asynchronous mechanisms. Important aspects involved in the BC-enabled FL optimization, such as the network size, link capacity, or user requirements, are put together and analyzed. As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case. Nevertheless, asynchronous federated optimization provides much lower latency in many cases, thus becoming an appealing FL solution when dealing with large data sets, tough timing constraints (e.g., near-real-time applications), or highly varying training data.

Related papers

• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• FADAS: Towards Federated Adaptive Asynchronous Optimization [56.09666452175333]
  Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. This paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated optimization with provable guarantees. We rigorously establish the convergence rate of the proposed algorithms and empirical results demonstrate the superior performance of FADAS over other asynchronous FL baselines.
  arXiv  Detail & Related papers  (2024-07-25T20:02:57Z)
• FedAST: Federated Asynchronous Simultaneous Training [27.492821176616815]
  Federated Learning (FL) enables devices or clients to collaboratively train machine learning (ML) models without sharing their private data. Much of the existing work in FL focuses on efficiently learning a model for a single task. In this paper, we propose simultaneous training of multiple FL models using a common set of datasets.
  arXiv  Detail & Related papers  (2024-06-01T05:14:20Z)
• Momentum Approximation in Asynchronous Private Federated Learning [26.57367597853813]
  momentum approximation can achieve $1.15 textrm--4times$ speed up in convergence compared to existing FLs with momentum. Momentum approximation can be easily integrated in production FL systems with a minor communication and storage cost.
  arXiv  Detail & Related papers  (2024-02-14T15:35:53Z)
• AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices [61.66943750584406]
  We propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments. First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence. Second, we propose a dynamic staleness-aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce communication and computation costs without significant accuracy degradation.
  arXiv  Detail & Related papers  (2023-12-18T05:18:17Z)
• 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)
• 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)
• Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout [22.584080337157168]
  Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup. We propose textttAsyncDrop, a novel asynchronous FL framework that utilizes dropout regularization to handle device heterogeneity in distributed settings. Overall, textttAsyncDrop achieves better performance compared to state of the art asynchronous methodologies.
  arXiv  Detail & Related papers  (2022-10-28T13:00:29Z)
• Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning [72.78668894576515]
  Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework. We propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems.
  arXiv  Detail & Related papers  (2021-07-23T18:57:08Z)
• Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients [21.63719641718363]
  Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices. This paper presents a novel FL algorithm, namely Hybrid Federated Learning (HFL), to achieve a learning balance in efficiency and effectiveness.
  arXiv  Detail & Related papers  (2021-02-12T02:27:44Z)

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.