arXiv:2306.02216v3 Announce Type: replace Abstract: Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address the "right to be forgotten" and to remove the influence of poisoned or target clients without retraining the entire FL system. However, many FU methods require communication with retained or target clients, introduce additional security risks, or store historical models, limiting their efficiency and practicality. Moreover, most FU methods for deep neural networks (DN
The increasing focus on data privacy regulations like 'right to be forgotten' combined with the widespread adoption of Federated Learning necessitates practical solutions for data unlearning.
Improving Federated Unlearning makes distributed AI systems more compliant with privacy laws and more resilient to poisoned data, enhancing trust and accelerating adoption of privacy-preserving AI.
The ability to efficiently remove the influence of specific data without full retraining fundamentally alters how privacy and security are managed in federated AI deployments.
- · AI developers
- · Organizations using federated learning
- · Data privacy advocates
- · Cloud providers
- · Attackers attempting data poisoning
- · Legacy unlearning methods
More robust and privacy-compliant federated AI models can be deployed in sensitive sectors like healthcare and finance.
Reduced regulatory hurdles might accelerate the development and adoption of AI applications dependent on distributed, sensitive user data.
The enhanced data sovereignty could encourage nations to adopt federated AI solutions for domestic data, supporting 'sovereign AI' initiatives.
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