Proof-of-Data: A Consensus Protocol for Collaborative Intelligence

• URL: http://arxiv.org/abs/2501.02971v1
• Date: Mon, 06 Jan 2025 12:27:59 GMT
• Title: Proof-of-Data: A Consensus Protocol for Collaborative Intelligence
• Authors: Huiwen Liu, Feida Zhu, Ling Cheng,
• Abstract summary: We propose a blockchain-based Byzantine fault-tolerant federated learning framework based on a novel Proof-of-Data (PoD) consensus protocol. PoD is able to enjoy the benefit of learning efficiency and system liveliness from societal-scale PoW-style learning. To mitigate false reward claims by data forgery from Byzantine attacks, a privacy-aware data verification and contribution-based reward allocation mechanism is designed to complete the framework.
• Score: 6.107950942979923
• License:
• Abstract: Existing research on federated learning has been focused on the setting where learning is coordinated by a centralized entity. Yet the greatest potential of future collaborative intelligence would be unleashed in a more open and democratized setting with no central entity in a dominant role, referred to as "decentralized federated learning". New challenges arise accordingly in achieving both correct model training and fair reward allocation with collective effort among all participating nodes, especially with the threat of the Byzantine node jeopardising both tasks. In this paper, we propose a blockchain-based decentralized Byzantine fault-tolerant federated learning framework based on a novel Proof-of-Data (PoD) consensus protocol to resolve both the "trust" and "incentive" components. By decoupling model training and contribution accounting, PoD is able to enjoy not only the benefit of learning efficiency and system liveliness from asynchronous societal-scale PoW-style learning but also the finality of consensus and reward allocation from epoch-based BFT-style voting. To mitigate false reward claims by data forgery from Byzantine attacks, a privacy-aware data verification and contribution-based reward allocation mechanism is designed to complete the framework. Our evaluation results show that PoD demonstrates performance in model training close to that of the centralized counterpart while achieving trust in consensus and fairness for reward allocation with a fault tolerance ratio of 1/3.

Related papers

• Protocol Learning, Decentralized Frontier Risk and the No-Off Problem [56.74434512241989]
  We identify a third paradigm - Protocol Learning - where models are trained across decentralized networks of incentivized participants. This approach has the potential to aggregate orders of magnitude more computational resources than any single centralized entity. It also introduces novel challenges: heterogeneous and unreliable nodes, malicious participants, the need for unextractable models to preserve incentives, and complex governance dynamics.
  arXiv  Detail & Related papers  (2024-12-10T19:53:50Z)
• Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm [2.5203968759841158]
  We propose Proof-of-Collaborative-Learning (PoCL), a multi-winner federated learning validated consensus mechanism. PoCL redirects the power of blockchains to train federated learning models. We present a novel evaluation mechanism to ensure the efficiency of the locally trained models of miners.
  arXiv  Detail & Related papers  (2024-07-17T21:14:05Z)
• 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)
• Combating Exacerbated Heterogeneity for Robust Models in Federated Learning [91.88122934924435]
  Combination of adversarial training and federated learning can lead to the undesired robustness deterioration. We propose a novel framework called Slack Federated Adversarial Training (SFAT) We verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets.
  arXiv  Detail & Related papers  (2023-03-01T06:16:15Z)
• Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping [77.53019031244908]
  We present a novel decentralized learning framework based on nonlinear gossiping (NGO), that enjoys an appealing finite-time consensus property to achieve better synchronization. Our analysis on how communication delay and randomized chats affect learning further enables the derivation of practical variants.
  arXiv  Detail & Related papers  (2021-11-04T15:36:25Z)
• Blockchain-based Trustworthy Federated Learning Architecture [16.062545221270337]
  We present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. We also propose a weighted fair data sampler algorithm to enhance fairness in training data.
  arXiv  Detail & Related papers  (2021-08-16T06:13:58Z)
• Secure Distributed Training at Scale [65.7538150168154]
  Training in presence of peers requires specialized distributed training algorithms with Byzantine tolerance. We propose a novel protocol for secure (Byzantine-tolerant) decentralized training that emphasizes communication efficiency.
  arXiv  Detail & Related papers  (2021-06-21T17:00:42Z)
• 2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments [9.885896204530878]
  We introduce 2CP, a framework comprising two novel protocols for Federated Learning. Crowdsource Protocol allows an actor to bring a model forward for training, and use their own data to evaluate the contributions made to it. The Consortium Protocol gives trainers the same guarantee even when no party owns the initial model and no dataset is available.
  arXiv  Detail & Related papers  (2020-11-15T12:59:56Z)
• BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning [2.0625936401496237]
  BlockFLow is an accountable federated learning system that is fully decentralized and privacy-preserving. Its primary goal is to reward agents proportional to the quality of their contribution while protecting the privacy of the underlying datasets and being resilient to malicious adversaries.
  arXiv  Detail & Related papers  (2020-07-08T02:24:26Z)
• Byzantine-resilient Decentralized Stochastic Gradient Descent [85.15773446094576]
  We present an in-depth study towards the Byzantine resilience of decentralized learning systems. We propose UBAR, a novel algorithm to enhance decentralized learning with Byzantine Fault Tolerance.
  arXiv  Detail & Related papers  (2020-02-20T05:11:04Z)

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.