Federated Learning and Machine Unlearning: Emerging Trends and Innovations
Recent advancements in federated learning (FL) and machine unlearning (MU) have significantly shaped the landscape of privacy-preserving machine learning. Federated learning continues to evolve, addressing the need for collaborative model training while safeguarding individual data privacy. Innovations in federated unlearning, such as adaptive differential privacy preservation and accelerated unlearning methods, are making strides in efficiently erasing specific data influences without compromising model performance.
In the realm of machine unlearning, the focus has shifted towards developing methods that not only ensure the removal of specific data but also maintain the integrity and performance of the model. Techniques leveraging low-rank updates and dynamic stopping mechanisms are proving to be effective in reducing computational costs while enhancing unlearning efficiency. Additionally, the integration of explainable AI (XAI) for verifying unlearning processes is emerging as a critical area, ensuring compliance with privacy regulations and enhancing trust in AI systems.
The field is also witnessing a growing interest in federated incremental learning, particularly in scenarios where new entity types and clients are continuously introduced. This dynamic environment necessitates models that can adapt and retain previously learned knowledge, addressing the challenges of heterogeneous forgetting.
Noteworthy developments include:
- Efficient Federated Unlearning with Adaptive Differential Privacy Preservation: Introduces a novel approach to balance unlearning efficiency with privacy protection.
- FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method: Proposes an exact unlearning approach that enhances efficiency and preserves model performance.
- Federated Incremental Named Entity Recognition: Addresses the challenges of continuous entity type updates and new client integrations in federated learning.
- Verifying Machine Unlearning with Explainable AI: Explores the use of XAI for effective verification of unlearning processes, enhancing regulatory compliance.