Using Federated Learning to develop AI models on personally identifiable information
Keywords:
Federated Learning, Sensitive Data, Artificial Intelligence, Data SecurityAbstract
Building AI models on sensitive data presents both opportunities & challenges as artificial intelligence becomes increasingly entwined into numerous sectors. Conventional approaches of AI model development rely on centralized systems, so large datasets are gathered & handled on a single server. Although this approach is doable, especially for the personally identifiable or sensitive data, it seriously compromises privacy & the security. By enabling the training of Artificial Intelligence models straight on the distributed data sources, hence removing the need to transport sensitive data to a central repository, Federated Learning (FL) offers an efficient answer to these problems. Because of the data privacy is kept confined to its source, this distributed approach protects it. By aggregating the model updates from many sources instead of utilizing the raw data, federated learning assures that data stays in its natural position, therefore lowering the risk of data breaches & ensuring the adherence to strict data security regulations like GDPR. The basic ideas of federated learning—including architecture, key components & the need of secure aggregation with methods in maintaining the anonymity—are discussed in this article. It also emphasizes the growing range of uses for federated learning—spanning healthcare, finance & the mobile devices—where data privacy is very vital. While acknowledging the difficulties of communications efficiency, model synchronizing & the complexity of huge-scale FL implementation, the paper investigates the advantages of federated learning (FL), including enhanced privacy, reduced bandwidth usage & the improved model performance via collaborative learning, underline.
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