Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
arXiv:2605.20341v1 Announce Type: new Abstract: Federated learning systems must support data deletion requests to comply with privacy regulations, yet retraining from scratch after each deletion is computationally prohibitive. We present HF-KCU, a method that removes a client's contribution by approximating the influence function through conjugate gradient iterations in Krylov subspaces, reducing complexity from O(d^3) to O(kd) where k<<d.A causal weighting mechanism ensures that only clients holding the deleted data receive parameter updates, preventing spurious changes to unaffected clients.
The increasing adoption of federated learning in privacy-sensitive domains and the evolving landscape of data privacy regulations (e.g., GDPR) necessitate efficient data deletion mechanisms.
This breakthrough addresses a critical challenge for federated learning, enabling compliance with data privacy regulations without prohibitive computational cost, thus accelerating its practical deployment.
The ability to efficiently 'unlearn' data in federated systems shifts the feasibility of large-scale privacy-preserving AI, moving it from a theoretical ideal to a practical engineering problem.
- · Federated Learning providers
- · Privacy-focused AI applications
- · Cloud computing platforms
- · Regulatory bodies
- · Organizations with rigid data retention policies
- · Traditional centralized AI training methods
Federated learning becomes more viable for applications requiring stringent privacy compliance and dynamic data management.
This could lead to a proliferation of privacy-preserving AI services across various sectors, from healthcare to finance.
Increased trust in AI systems due to improved privacy guarantees might accelerate public adoption and integration into daily life, potentially necessitating new ethical frameworks for 'unlearned' information.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG