When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain

• URL: http://arxiv.org/abs/2406.04743v1
• Date: Fri, 7 Jun 2024 08:42:26 GMT
• Title: When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain
• Authors: Lei Xu, Yulong Chen, Yuntian Chen, Longfeng Nie, Xuetao Wei, Liang Xue, Dongxiao Zhang,
• Abstract summary: Machine learning models offer the capability to forecast future energy production or consumption. However, legal and policy constraints within specific energy sectors present technical hurdles in utilizing data from diverse sources. We propose adopting a Swarm Learning scheme, which replaces the centralized server with a blockchain-based distributed network.
• Score: 10.099134773737939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models offer the capability to forecast future energy production or consumption and infer essential unknown variables from existing data. However, legal and policy constraints within specific energy sectors render the data sensitive, presenting technical hurdles in utilizing data from diverse sources. Therefore, we propose adopting a Swarm Learning (SL) scheme, which replaces the centralized server with a blockchain-based distributed network to address the security and privacy issues inherent in Federated Learning (FL)'s centralized architecture. Within this distributed Collaborative Learning framework, each participating organization governs nodes for inter-organizational communication. Devices from various organizations utilize smart contracts for parameter uploading and retrieval. Consensus mechanism ensures distributed consistency throughout the learning process, guarantees the transparent trustworthiness and immutability of parameters on-chain. The efficacy of the proposed framework is substantiated across three real-world energy series modeling scenarios with superior performance compared to Local Learning approaches, simultaneously emphasizing enhanced data security and privacy over Centralized Learning and FL method. Notably, as the number of data volume and the count of local epochs increases within a threshold, there is an improvement in model performance accompanied by a reduction in the variance of performance errors. Consequently, this leads to an increased stability and reliability in the outcomes produced by the model.

Related papers

• SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment [4.925906256430176]
  Federated Learning (FL) has emerged as a transformative approach for enabling distributed machine learning while preserving user privacy. This paper presents a novel FL methodology that overcomes these limitations by eliminating the dependency on edge servers.
  arXiv  Detail & Related papers  (2024-07-25T20:42:16Z)
• 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)
• Blockchain-enabled Trustworthy Federated Unlearning [50.01101423318312]
  Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. Existing works require central servers to retain the historical model parameters from distributed clients. This paper proposes a new blockchain-enabled trustworthy federated unlearning framework.
  arXiv  Detail & Related papers  (2024-01-29T07:04:48Z)
• Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity [14.313847382199059]
  Federated quantization-based self-supervised learning scheme (Fed-QSSL) designed to address heterogeneity in FL systems. Fed-QSSL deploys de-quantization, weighted aggregation and re-quantization, ultimately creating models personalized to both data distribution and specific infrastructure of each client's device.
  arXiv  Detail & Related papers  (2023-12-20T19:11:19Z)
• Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality [0.0]
  The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism. The proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy.
  arXiv  Detail & Related papers  (2023-10-30T06:05:50Z)
• BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model [8.19957400564017]
  Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant attention for addressing this concern. A challenge arises in federated learning due to the Byzantine Attack Problem, where malicious local models can compromise the global model's performance during aggregation. This article proposes the integration of Byzantine-Robust Federated Learning (BRLF) model that combines federated learning with blockchain technology.
  arXiv  Detail & Related papers  (2023-10-20T10:21:50Z)
• Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
  Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC. We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
  arXiv  Detail & Related papers  (2023-07-14T04:13:11Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization [22.61730495802799]
  We present a system design which offers efficient protection of individual model updates throughout the learning procedure. Our system achieves accuracy comparable to the baseline, with practical performance.
  arXiv  Detail & Related papers  (2022-02-04T05:03:46Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [119.19061102064497]
  We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL) In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round. We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
  arXiv  Detail & Related papers  (2021-01-18T07:19:08Z)

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.