Towards a Secure and Reliable Federated Learning using Blockchain (2201.11311v1)

Abstract: Federated learning (FL) is a distributed ML technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB-FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.

Towards a Secure and Reliable Federated Learning using Blockchain

The paper "Towards a Secure and Reliable Federated Learning using Blockchain" addresses critical challenges in federated learning (FL) by introducing a blockchain-based framework named SRB-FL. Federated learning, a decentralized machine learning paradigm, allows devices to collaboratively train models while maintaining data privacy. However, issues such as device reliability, communication inefficiencies, and model update integrity present significant hurdles. This paper proposes leveraging blockchain technology, particularly the concept of sharding, to enhance the robustness, scalability, and security of FL systems.

Key Contributions

The SRB-FL framework integrates blockchain to resolve FL's inherent challenges through the following innovations:

1. Blockchain Sharding: By subdividing the blockchain into shards, the framework supports parallel processing, thereby improving scalability and reducing latency. Each shard manages its subset of FL devices, facilitating efficient collaboration and model training.
2. Incentive Mechanism: The authors introduce an incentive mechanism utilizing multi-weight subjective logic to assess and enhance the reliability of participating devices. This system rewards honest behavior and penalizes malicious activities, thus fostering a trustworthy learning environment.
3. Reputation System: The paper implements an inter- and intra-shard reputation system to evaluate device reliability accurately. This system applies weighted subjective logic, considering factors such as interactivity and novelty of device interactions, to ensure reliable model updates.

Methodology and Evaluation

The SRB-FL framework is tested using the MNIST dataset and the PyTorch environment to understand its performance in terms of scalability, accuracy, reputation, and latency:

• Scalability: The incorporation of blockchain sharding significantly reduces the growth rate of transaction numbers over time compared to traditional PoW-based blockchain systems. This improvement is critical for large-scale FL deployments.
• Accuracy: The framework demonstrates robustness against unreliable devices. With its reputation system, SRB-FL maintains higher model accuracy than comparable FL-blockchain systems.
• Reputation and Reliability: The multi-weight subjective logic mechanism proves effective in lowering the reputation scores of malicious devices more sharply than existing systems, ensuring only reliable devices influence model training.
• Latency: SRB-FL maintains consistent latency in adding blocks to the blockchain, enhancing the overall efficiency of the FL process.

Implications and Future Work

The introduction of a blockchain-enhanced FL framework has significant implications for secure and efficient distributed learning:

• Scalable and Trustworthy Collaboration: By enabling extensive and reliable device collaboration, SRB-FL can be widely applied in areas such as autonomous vehicles, healthcare data analytics, and smart grids, where data privacy and model integrity are paramount.
• Future Innovations: Further research may explore integrating additional optimization techniques or exploring new consensus mechanisms to complement sharding, further enhancing FL efficiency. Potential developments might include adapting the framework for other privacy-preserving techniques and deploying it in real-world scenarios.

Overall, the SRB-FL framework offers a promising approach to overcoming critical challenges in federated learning, promoting a more secure and reliable computational environment. This research lays a foundation for future exploration into blockchain-integrated FL systems.