BlockFUL: Enabling Unlearning in Blockchained Federated Learning

• URL: http://arxiv.org/abs/2402.16294v2
• Date: Wed, 14 Aug 2024 10:44:53 GMT
• Title: BlockFUL: Enabling Unlearning in Blockchained Federated Learning
• Authors: Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu,
• Abstract summary: Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. In this paper, we introduce a novel framework with a dual-chain structure comprising a live chain and an archive chain for enabling unlearning capabilities withined FL. Two new unlearning paradigms, i.e., parallel and sequential paradigms, can be effectively implemented through gradient-ascent-based and re-training-based unlearning methods. Our experiments validate that these methods effectively reduce data dependency and operational overhead, thereby boosting the overall performance of unlearning inherited models within BlockFUL.
• Score: 26.47424619448623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchain is employed to ensure the integrity and traceability of FL, where the need to edit multiple interlinked blockchain records and update all inherited models complicates the process.In this paper, we introduce Blockchained Federated Unlearning (BlockFUL), a novel framework with a dual-chain structure comprising a live chain and an archive chain for enabling unlearning capabilities within Blockchained FL. BlockFUL introduces two new unlearning paradigms, i.e., parallel and sequential paradigms, which can be effectively implemented through gradient-ascent-based and re-training-based unlearning methods. These methods enhance the unlearning process across multiple inherited models by enabling efficient consensus operations and reducing computational costs. Our extensive experiments validate that these methods effectively reduce data dependency and operational overhead, thereby boosting the overall performance of unlearning inherited models within BlockFUL on CIFAR-10 and Fashion-MNIST datasets using AlexNet, ResNet18, and MobileNetV2 models.

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