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Federated learning (fl) presents a decentralized solution by enabling multiple devices to collaboratively train machine learning models without sharing raw data, thus preserving user privacy. The privacy-preserving federated learning for science: Building sustainable and trustworthy foundation models project aims to innovate the way large-scale artificial intelligence (ai) models are …
Large datasets have made astounding breakthroughs in machine learning possible. But oftentimes data is personal or proprietary, and not meant to be shared, making privacy a critical concern of and … This systematic review highlights the multifaceted nature of privacy preservation in federated learning (fl), underscoring the interplay of technical, legal, and ethical dimensions.
