The increasing availability of data generated by Internet of Things (IoT) and Industrial IoT (IIoT) devices, as well as privacy and law regulations, have significantly boosted the interest in collaborative machine learning (ML) approaches. In this direction, we claim federated learning (FL) as a promising ML paradigm where participants collaboratively train a global model without outsourcing on-premises data. However, setting up and using FL can be extremely costly and time consuming. To effectively promote the adoption of FL in real-world scenarios, while limiting the overhead and knowledge of the underlying technology, service providers should offer FL as a Service (FLaaS). One of the major concerns while designing an architecture that provides FLaaS is achieving trustworthiness among involved typically unknown participants. This article presents a blockchain-based architecture that achieves trustworthy FLaaS (TruFLaaS). Our solution provides trustworthiness among third-party organizations by leveraging blockchain, smart contracts, and a decentralized oracle network. Specifically, during each FL round, the service provider supplies a sample, without overlapping, of its validation set to validate all partial models submitted by clients. By doing so, poor models, which tend to degrade performance or introduce malicious backdoors, are identified and discarded. Due to the transparency of the blockchain, not changing the validation set would enable participants to forge a malicious partial model that passes the validation phase. We evaluate our approach over two well-known IIoT data sets: the reported experimental results show that TruFLaaS outperforms the state-of-the-art literature solutions in the field.

TruFLaaS: Trustworthy Federated Learning as a Service

Mazzocca, Carlo;Romandini, Nicolò;Mendula, Matteo;Montanari, Rebecca;Bellavista, Paolo
2023

Abstract

The increasing availability of data generated by Internet of Things (IoT) and Industrial IoT (IIoT) devices, as well as privacy and law regulations, have significantly boosted the interest in collaborative machine learning (ML) approaches. In this direction, we claim federated learning (FL) as a promising ML paradigm where participants collaboratively train a global model without outsourcing on-premises data. However, setting up and using FL can be extremely costly and time consuming. To effectively promote the adoption of FL in real-world scenarios, while limiting the overhead and knowledge of the underlying technology, service providers should offer FL as a Service (FLaaS). One of the major concerns while designing an architecture that provides FLaaS is achieving trustworthiness among involved typically unknown participants. This article presents a blockchain-based architecture that achieves trustworthy FLaaS (TruFLaaS). Our solution provides trustworthiness among third-party organizations by leveraging blockchain, smart contracts, and a decentralized oracle network. Specifically, during each FL round, the service provider supplies a sample, without overlapping, of its validation set to validate all partial models submitted by clients. By doing so, poor models, which tend to degrade performance or introduce malicious backdoors, are identified and discarded. Due to the transparency of the blockchain, not changing the validation set would enable participants to forge a malicious partial model that passes the validation phase. We evaluate our approach over two well-known IIoT data sets: the reported experimental results show that TruFLaaS outperforms the state-of-the-art literature solutions in the field.
2023
Mazzocca, Carlo; Romandini, Nicolò; Mendula, Matteo; Montanari, Rebecca; Bellavista, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/952081
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