A Blockchain Solution for Collaborative Machine Learning over IoT

The proliferation of Internet of Things (IoT) devices and applications has generated massive amounts of data that require advanced analytics and machine learning techniques for meaningful insights. However, traditional centralized machine learning models face challenges such as data privacy, security, and scalability. Federated learning (FL) [1] is an emerging technique that addresses these challenges by enabling decentralized model training on distributed data sources while preserving data privacy and security. Despite its promise, FL still faces several technical challenges such as non-iid data distribution, communication overhead, and straggler nodes [2]. In the traditional FL approach, multiple devices work together to train a machine learning model while retaining their data locally, without sharing it with other participating devices; thus, data resides on trusted nodes. This scenario is particularly convenient for IoT applications, where devices often generate sensitive data that must be protected from unauthorized access. Model updates are exchanged between these nodes for aggregation, contributing to enrich the global model without exposing their raw data. Consequently, by retaining their data locally and collaborating on model training through the exchange of model updates, the devices can effectively contribute to the learning process while maintaining data privacy and security. However, this exchange of model updates introduces new security and privacy concerns, as it makes the models potentially vulnerable to various types of attacks. Therefore, FL encounters additional security-related challenges, including data poisoning attacks where malicious nodes inject corrupted or misleading data into the training process, compromising the accuracy of the global model. Model inversion attacks pose another threat, as adversaries aim to reconstruct individual data samples from aggregated model updates, potentially revealing sensitive information. Furthermore, sibyl attacks occur when malicious entities create multiple fake nodes to disproportionately influence the federated learning process, and collusion attacks involve a group of malicious nodes conspiring to manipulate the global model [3]. To address these challenges, recent research has proposed FL solutions that leverage blockchain technology for secure and efficient data sharing, model training, and prototype storage in a distributed environment. Blockchain technology [4], by providing a tamper-proof distributed ledger for storing and sharing data, models, and training results, enables collaboration among multiple parties without the need for a central authority, thereby significantly enhancing data privacy and security in the process.