Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead

2025-10-01

AI Summary

In federated learning, the absence of verifiable data unlearning mechanisms prevents reliable proof that client data influence has been eliminated, hindering compliance with the “right to be forgotten.” To address this, we propose veriFUL—the first verifiable unlearning framework specifically designed for federated learning. It systematically formalizes the verifier, target, methodology, and quantitative metrics for unlearning verification, thereby filling a critical theoretical gap. Integrating principles from federated learning, decentralized computation, and formal verification, veriFUL embeds verifiability directly into the unlearning workflow, enabling third-party auditability and precise impact quantification. Experimental results demonstrate that veriFUL effectively validates the removal of specific data contributions and significantly enhances trust in model unlearning. This work establishes a privacy-compliant, trustworthy unlearning pathway for high-stakes domains such as healthcare.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated unlearning (FUL) enables removing the data influence from the model trained across distributed clients, upholding the right to be forgotten as mandated by privacy regulations. FUL facilitates a value exchange where clients gain privacy-preserving control over their data contributions, while service providers leverage decentralized computing and data freshness. However, this entire proposition is undermined because clients have no reliable way to verify that their data influence has been provably removed, as current metrics and simple notifications offer insufficient assurance. We envision unlearning verification becoming a pivotal and trust-by-design part of the FUL life-cycle development, essential for highly regulated and data-sensitive services and applications like healthcare. This article introduces veriFUL, a reference framework for verifiable FUL that formalizes verification entities, goals, approaches, and metrics. Specifically, we consolidate existing efforts and contribute new insights, concepts, and metrics to this domain. Finally, we highlight research challenges and identify potential applications and developments for verifiable FUL and veriFUL.

Problem

• Federated unlearning lacks reliable verification methods for data removal
• Current metrics and notifications provide insufficient assurance of data deletion
• Need trust-by-design verification for privacy-sensitive applications like healthcare

Innovation

• Verifiable federated unlearning framework veriFUL
• Formalizes verification entities and metrics
• Addresses data removal verification challenges