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

Thanh Linh Nguyen, Marcela Tuler de Oliveira, An Braeken, Aaron Yi Ding, Quoc-Viet Pham

TL;DR

Federated learning raises RTBF and data-removal concerns; existing unlearning methods lack verifiability. The paper proposes VeriFUL, a reference framework with entities, goals, approaches, and metrics to enable verifiable FUL and trust-by-design. It surveys cryptographic proofs, TEEs, DL auditing, and active testing, and defines completeness, timeliness, correctness, exclusivity, and reversibility as core goals with associated metrics. The approach aims to close the verification gap and guide practical deployment in regulated, privacy-sensitive applications such as healthcare.

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.

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

TL;DR

Federated learning raises RTBF and data-removal concerns; existing unlearning methods lack verifiability. The paper proposes VeriFUL, a reference framework with entities, goals, approaches, and metrics to enable verifiable FUL and trust-by-design. It surveys cryptographic proofs, TEEs, DL auditing, and active testing, and defines completeness, timeliness, correctness, exclusivity, and reversibility as core goals with associated metrics. The approach aims to close the verification gap and guide practical deployment in regulated, privacy-sensitive applications such as healthcare.

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.

Paper Structure

This paper contains 18 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. VeriFUL: VERIFIABLE FUL FRAMEWORK
  3. WHO - Verification Entities
  4. WHAT - Verification Goals
  5. HOW TO - Verification Approaches
  6. HOW WELL - Verification Metrics
  7. CHALLENGES
  8. The Co-Evolution of Unlearning Algorithms and Verifiability
  9. Efficiency vs. Privacy Compliance
  10. Privacy and Security
  11. Incentive mechanisms
  12. Scalability
  13. THE ROAD AHEAD
  14. Application-specific Solutions
  15. Target Client-Executed Unlearning
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Illustration of the evolution from centralized ML to FL and FUL, highlighting key challenges at each stage and the emerging need for verifiable FUL.
  • Figure 2: VeriFUL reference framework with workflow - we focus on Step 5 in this article.