Blockchain-Based Decentralized Model Aggregation for Cross-Silo Federated Learning in Industry 4.0

Abstract

Traditional Federated Learning (FL) adopts a clientserver architecture where FL clients (e.g. IoT edge devices) train a common global model with the help of a centralised orchestrator (cloud server). However, current approaches are moving away from centralised orchestration towards a decentralised one in order to fully adapt FL for a cross-silo configuration with multiple organisations acting as clients. State-of-the-art decentralised FL mechanisms make at least one of the following assumptions: (a) clients are trusted organisations and cannot inject low quality model updates for aggregation and (b) client local models can be shared with other clients or a third party for verification of low quality updates. This paper proposes a Blockchain based decentralised framework for scenarios where participatory organisations are believed to be fully capable of injecting low quality model updates as they are not willing to expose their local models to any other entity for verification purpose. The proposed decentralised FL framework adopts a novel hierarchical network of aggregators with the ability to punish/reward organisations in proportion to their local model quality updates. The framework is flexible and unlike state-ofthe-art solutions, prevents a single entity from possessing the aggregated model in any FL round of training. The proposed framework is tested with respect to off-chain and on-chain performance in two Industry 4.0 use-cases: predictive maintenance and product visual inspection. A comparative evaluation against the state-of-the-art reveals the proposed framework's utility in terms of minimizing model convergence time and latency while maximizing accuracy and throughput.

Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases

“…These challenges become even more pronounced and difficult to address in the context of industrial IoT (IIoT), where much higher levels of security, safety, and reliability are demanded in applications,such as shipping, smart ports, smart factories, smart manufacturing and smart transportation. In the area of PdM there have been various studies presenting FL-based anomaly detection with minimized computation cost for industrial control systems [17], autoencoder-based FL, using vibration sensor data from rotating machines, enabling distributed training on edge devices [18], split-learning-based PdM, facilitating FL clients to maximize available resources within their local network, and addressing orchestration, device heterogeneity and scalability issues [19], blockchain-based FL, relying on a hierarchical aggregator network, punishing and rewarding clients according to their local model quality updates [20]. Still, the integration of FL-aided prognostics in Shipping 4.0 applications has been largely missing from the literature and in this study, we aim to fill this gap by carefully tackling a variety of maritime use cases.…”

A Data Attack Detection Framework for Cryptography-Based Secure Aggregation Methods in 6G Intelligent Applications

“…In a 6G network, by designing smart contracts, this mechanism can evaluate participants' contributions and distribute rewards accordingly without disclosing personal data, thus promoting the contribution of high-quality data. T. Ranathunga et al [31] proposed a blockchain-based decentralized federated-learning framework that uses an aggregator of hierarchical networks to reward or penalize organizations based on the quality of local models.…”