Federated learning in AI is a decentralized approach where the model is trained across multiple devices or servers holding local data samples, without exchanging them. Instead of sending all the data to a central server for training, the model is sent to the data, and the updated model is then aggregated without the raw data leaving the device. This approach helps in preserving privacy as it reduces the risk of exposing sensitive data to third parties. By keeping the data on the device and only sharing model updates, federated learning minimizes the potential for privacy breaches and data leaks. It allows for AI models to be trained on a large amount of diverse data without compromising the privacy of individual users.
To explain federated learning in a simple way, imagine a group of students studying for a test. Instead of sharing their personal notes with each other, they decide to study together and then share their individual improvements with the group. This way, each student’s personal notes remain private, but the group benefits from everyone’s collective knowledge. Similarly, in federated learning, individual devices collaborate to improve the AI model without sharing their private data, thus ensuring privacy while still achieving progress in the model’s training.
Please note that the provided answer is a brief overview; for a comprehensive exploration of privacy, privacy-enhancing technologies, and privacy engineering, as well as the innovative contributions from our students at Carnegie Mellon’s Privacy Engineering program, we highly encourage you to delve into our in-depth articles available through our homepage at https://privacy-engineering-cmu.github.io/.
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