Federated Learning for Privacy-Preserving Artificial Intelligence Applications
Keywords:
Distributed learning3, Edge computing, Federated learning, Homomorphic encryption, Internet of Things (IoT), Privacy-preserving machine learning, Secure AggregationAbstract
Artificial Intelligence (AI) technology plays a significant role in the development of various solutions to address complex challenges in various sectors such as healthcare, banking, logistics, transport, and mobile computing, among others. Current artificial intelligence technologies rely on extensive amounts of data for the effective training of machine learning algorithms. Usually, data for training machine learning algorithms has been collected on a central server that performs the training of machine learning algorithms. However, collecting and storing data on the central server poses major threats to data privacy, security breaches, and legal obligations, especially for personal and organizational data that require protection. As such, in order to mitigate the above challenges, a new paradigm in machine learning algorithms referred to as federated learning has been developed. Under federated learning, machine learning models are usually trained at the client-side, which could include a smartphone, edge devices, or an organizational server. In this case, the client device would not transmit actual data to the central server but the parameters of the machine learning algorithms instead. Here, the central server would use different aggregation algorithms in the combination of the parameters of the machine learning models from multiple client devices. Communication cost would also be lowered because model parameters would only be communicated to the central server instead of actual data. More importantly, federated learning enables organizations to collaborate in building intelligent machines without violating any privacy and data protection rules.
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