Reliable Federated Learning for Mobile Networks (1910.06837v1)

Abstract: Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, e.g., mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In the federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, e.g., the data poisoning attack, or unintentionally, e.g., low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.

• The paper introduces a reputation-based worker selection scheme that leverages consortium blockchain to mitigate unreliable data updates in federated learning.
• It employs a multi-weight subjective logic model to assess worker trustworthiness based on historical performance and interaction records.
• Experiments on the MNIST dataset show significant improvements in model accuracy and robustness against data poisoning attacks.

Reliable Federated Learning for Mobile Networks

The paper "Reliable Federated Learning for Mobile Networks" addresses the crucial challenge of ensuring reliable and trustworthy federated learning (FL) in the context of mobile networks. The authors focus on the problem of unreliable data updates in federated learning systems, which can be detrimental to the performance and security of machine learning models. The paper introduces a reputation-based worker selection scheme leveraging consortium blockchain technology to mitigate these issues.

Key Concepts and Contributions

The authors highlight that federated learning is a promising approach that enables collaborative model training across multiple devices while preserving data privacy. However, FL systems are vulnerable to adversarial attacks, including data poisoning, where malicious devices provide fraudulent updates to degrade the model's accuracy. Furthermore, unintentional factors such as low-quality data due to energy constraints or mobility can also affect the reliability of FL.

Reputation Metric for Worker Selection: To combat these challenges, the authors propose using a reputation metric as a critical tool to evaluate and select reliable worker devices for federated learning tasks. The reputation metric reflects the trustworthiness of devices based on their historical behavior in previous tasks.

Consortium Blockchain for Reputation Management: The paper innovatively applies consortium blockchain as a decentralized ledger for efficient reputation management. This blockchain records and manages reputation opinions without repudiation and tampering. The use of pre-selected miners in consortium blockchains allows for lightweight and swift consensus processes suitable for mobile environments.

The major contributions of the paper encompass:

1. Introduction of a reputation-based method to select trustworthy workers, enhancing the accuracy and robustness of federated learning models.
2. Application of a multi-weight subjective logic model to efficiently calculate reputation by considering interaction histories and recommended opinions from other task publishers.
3. Deployment of consortium blockchain technology at the edge nodes for secure, decentralized management of reputational data, ensuring integrity and openness.

Evaluation and Results

The authors validate their approach through numerical simulations using the MNIST dataset. The simulation setup includes different classes of workers, including honest, unreliable, and malicious workers engaged in data poisoning attacks. The application of the proposed reputation-based selection scheme demonstrates significant improvements in the overall learning accuracy compared to traditional selection methods. Reputation management via blockchain achieves rapid detection and isolation of unreliable nodes, ensuring that high-quality data contributes to the global model.

Implications and Future Directions

The paper posits that the integration of reputation metrics and blockchain technology not only addresses security vulnerabilities but also enhances the efficacy of federated learning in mobile networks. It suggests several avenues for future work:

• Developing more sophisticated model update validation frameworks suited for non-IID (non-independent and identically distributed) datasets.
• Investigating optimal worker selection mechanisms balancing performance and computational overhead.
• Exploring dynamic threshold optimization strategies in reputation systems to adaptively respond to different levels of adversarial risk.

Overall, this paper presents a robust framework for enhancing the reliability of federated learning in mobile networks, providing a foundational step towards secure, decentralized AI training methodologies. Its methodologies and results are poised to influence future research and development in secure distributed learning frameworks, ensuring practical applicability in real-world mobile network deployments.