๐€๐๐ฏ๐š๐ง๐œ๐ข๐ง๐  ๐…๐ž๐๐ž๐ซ๐š๐ญ๐ž๐ ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐ฐ๐ข๐ญ๐ก ๐„๐Ÿ๐Ÿ๐ข๐œ๐ข๐ž๐ง๐ญ '๐”๐ง๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐ '๐Ÿš€


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Reference Paper : here


With privacy regulations empowering users with the right to be forgotten, it's essential to enable federated learning models to forget specific clients without retraining from scratch. This paper introduces a novel method that removes the influence of a client's data while significantly reducing communication and computation costs.


๐™†๐™š๐™ฎ ๐™ƒ๐™ž๐™œ๐™๐™ก๐™ž๐™œ๐™๐™ฉ๐™จ:


Efficient Federated Unlearning: Removes a client's data influence using local unlearning followed by minimal federated learning rounds.


Performance: Achieves results comparable to complete retraining but with 5x to 24x reduced costs.


Privacy-Enhanced: No need to store global or client update histories.


Innovative Approach: Utilizes Projected Gradient Descent (PGD) for local unlearning by maximizing the loss function to effectively remove a clientโ€™s data influence.


๐˜ผ๐™™๐™™๐™ž๐™ฉ๐™ž๐™ค๐™ฃ๐™–๐™ก ๐™„๐™ฃ๐™จ๐™ž๐™œ๐™๐™ฉ๐™จ:


Scalability and Generalizability: Needs further evaluation across diverse datasets and real-world applications.


Single Client Focus: Primarily designed for unlearning a single client; multiple client unlearning requires exploration.


These advancements are crucial for developing robust, privacy-preserving federated learning systems, especially in sectors with stringent privacy requirements.