Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions

2026-05-22 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

HF-KCU is a novel method for efficient federated unlearning, addressing the computational burden of data deletion requests in distributed machine learning systems. It approximates influence functions using Hessian-free conjugate gradient iterations within Krylov subspaces, reducing complexity from O(d^3) to O(kd) where k « d. A causal weighting mechanism ensures only clients holding deleted data receive parameter updates, preventing spurious changes. HF-KCU demonstrates a 47.75x speedup over retraining on CIFAR-10, maintaining test accuracy within 0.60% (71.16% vs 71.76%). It also achieves membership inference attack success rates of 0.499, matching retrained models, and mitigates backdoor attacks with a 430x speedup. The method is robust to bounded adversarial perturbations and provides convergence guarantees.

Key takeaway

For MLOps Engineers managing federated learning systems, HF-KCU provides a robust solution for handling data deletion requests. You can achieve up to 47.75x speedup over full retraining, maintaining model accuracy within 0.60% and ensuring privacy compliance. Consider integrating this Hessian-free Krylov method to efficiently meet GDPR and CCPA "right to be forgotten" requirements, especially for large models or frequent unlearning tasks, without compromising model utility or stability.

Key insights

HF-KCU enables efficient, accurate, and privacy-preserving federated unlearning by approximating influence functions with Hessian-free Krylov methods.

Principles

• Causal weighting ensures updates only affect clients with deleted data.
• Hessian-free Krylov approximation reduces unlearning complexity to O(kd).
• Approximation error decreases exponentially with CG iterations.

Method

HF-KCU approximates H⁻¹g via conjugate gradient iterations in Krylov subspaces, using automatic differentiation for Hessian-vector products. Causal weighting localizes updates to clients with deleted data.

In practice

• Achieve 47.75x speedup for federated data deletion.
• Rapidly mitigate backdoor attacks with 430x speedup.

Topics

• Federated Unlearning
• Hessian-Free Optimization
• Krylov Subspace Methods
• Privacy-Preserving AI
• Data Deletion
• Backdoor Mitigation

Best for: Research Scientist, AI Engineer, CTO, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.