A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization

• URL: http://arxiv.org/abs/2310.09665v1
• Date: Sat, 14 Oct 2023 20:47:30 GMT
• Title: A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization
• Authors: Xiao Li and Weili Wu
• Abstract summary: Federated learning (FL) is emerging as a sought-after distributed machine learning architecture. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. While blockchain technology promises to bolster security, practical deployment on resource-constrained edge devices remains a challenge.
• Score: 8.082460100928358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure of raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to FL in the context. While blockchain technology promises to bolster security, practical deployment on resource-constrained edge devices remains a challenge. Moreover, the exploration of FL with multiple aggregators in edge computing is still new in the literature. Addressing these gaps, we introduce the Blockchain-empowered Heterogeneous Multi-Aggregator Federated Learning Architecture (BMA-FL). We design a novel light-weight Byzantine consensus mechanism, namely PBCM, to enable secure and fast model aggregation and synchronization in BMA-FL. We also dive into the heterogeneity problem in BMA-FL that the aggregators are associated with varied number of connected trainers with Non-IID data distributions and diverse training speed. We proposed a multi-agent deep reinforcement learning algorithm to help aggregators decide the best training strategies. The experiments on real-word datasets demonstrate the efficiency of BMA-FL to achieve better models faster than baselines, showing the efficacy of PBCM and proposed deep reinforcement learning algorithm.

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)
• TDML -- A Trustworthy Distributed Machine Learning Framework [7.302091381583343]
  The rapid advancement of large models (LM) has intensified the demand for computing resources. This demand is exacerbated by limited availability due to supply chain delays and monopolistic acquisition by major tech firms. We propose a textittrustworthy distributed machine learning (TDML) framework that leverages guidance to coordinate remote trainers and validate workloads.
  arXiv  Detail & Related papers  (2024-07-10T03:22:28Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• BlockFUL: Enabling Unlearning in Blockchained Federated Learning [26.47424619448623]
  Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. In this paper, we introduce a novel framework with a dual-chain structure comprising a live chain and an archive chain for enabling unlearning capabilities withined FL. Two new unlearning paradigms, i.e., parallel and sequential paradigms, can be effectively implemented through gradient-ascent-based and re-training-based unlearning methods. Our experiments validate that these methods effectively reduce data dependency and operational overhead, thereby boosting the overall performance of unlearning inherited models within BlockFUL.
  arXiv  Detail & Related papers  (2024-02-26T04:31:53Z)
• Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach [2.5477011559292175]
  Federated learning (FL) offers a decentralized training approach for machine learning models, prioritizing data privacy. In this paper, we introduce a dynamic Upper Confidence Bound (dUCB) algorithm inspired by the multi-armed bandit (MAB) approach.
  arXiv  Detail & Related papers  (2023-10-29T16:46:50Z)
• Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks [49.19502459827366]
  Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-to-server (D2S) interactions.
  arXiv  Detail & Related papers  (2023-03-15T04:41:36Z)
• Trustworthy Federated Learning via Blockchain [30.887469477336783]
  federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. Security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server. We propose a decentralized FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices. We show that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm.
  arXiv  Detail & Related papers  (2022-08-13T03:43:10Z)
• Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing [24.505675843652448]
  In this paper, we study a new latency optimization problem for federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to handle both machine learning (ML) model training and block mining simultaneously.
  arXiv  Detail & Related papers  (2022-03-18T00:38:29Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks [88.15736037284408]
  We study a new model of multiple federated learning services at the multi-access edge computing server. We propose a joint resource optimization and hyper-learning rate control problem, namely MS-FEDL. Our simulation results demonstrate the convergence performance of our proposed algorithms.
  arXiv  Detail & Related papers  (2020-11-25T01:29:41Z)
• Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach [54.29213445674221]
  Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO) The issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency. We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for theO.
  arXiv  Detail & Related papers  (2020-04-08T16:29:19Z)

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.