Verifiable End-to-End Decentralized Federated Learning with Non-Disclosing Data Authenticity Proofs

Core Concepts

A verifiable decentralized federated learning system that extends existing blockchain and zero-knowledge proof-based approaches to provide end-to-end integrity and authenticity of data and computation without disclosing confidential information.

Abstract

The paper proposes a verifiable end-to-end decentralized federated learning (FL) system that addresses the limitations of existing approaches. The key contributions are:

System Model:

Extends previous verifiable decentralized FL systems by integrating certified sensor devices as data sources.
Identifies the inherent conflict between confidentiality and transparency in verifying data authenticity and device identities.

Two-Step Proving and Verification (2PV) Procedure:

Registration Workflow: Enables non-disclosing verification of device certificates on the blockchain.
Learning Workflow: Extends existing blockchain and zero-knowledge proof-based FL systems through non-disclosing data authenticity proofs.

Prototype Implementation and Evaluation:

Implements the proposed system as a proof-of-concept, building upon a reference implementation of a verifiable decentralized FL system.
Evaluates the technical feasibility, demonstrating only marginal overheads compared to the state-of-the-art.

The proposed system achieves end-to-end verifiability of data and computation in decentralized FL settings, addressing the limitations of previous approaches that could not verify the authenticity of the learning data.

Stats

The paper does not provide specific numerical data or metrics. The evaluation focuses on the technical feasibility and overhead of the proposed system compared to the reference implementation.

Quotes

"Verifiable decentralized FL through blockchains removes the need for a central aggregator that may fail or corrupt the result aggregation unnoticeably."
"To prevent model poisoning, verifiable decentralized FL systems have been proposed that advance blockchain-based FL through verifiable off-chain computation (VOC) where local model updates are executed using zero-knowledge proofs (ZKP)."
"Addressing this problem, in this paper, we suggest a first end-to-end verifiable decentralized FL system that makes the integrity and authenticity of data and computation verifiable, from certified edge devices to the blockchains secure parameter storage."

Key Insights Distilled From

End-to-End Verifiable Decentralized Federated Learning

by Chaehyeon Le... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12623.pdf

Deeper Inquiries

How can the proposed system be extended to support dynamic device registration and deregistration, and handle device revocation or replacement?

To support dynamic device registration and deregistration, the system can implement a mechanism where devices can register and deregister themselves autonomously. This can be achieved by allowing devices to generate their own certificates and submit them to the Certificate Authority (CA) for verification. Upon successful verification, the device can be added to the system. Similarly, for deregistration, devices can request to be removed from the system by submitting a deregistration request to the CA.
For device revocation or replacement, the system can introduce a revocation list maintained by the CA. If a device is compromised or needs to be replaced, the CA can revoke the device's certificate and update the revocation list. When a device presents its certificate for registration, the system can check the revocation list to ensure that the device is still valid. In the case of device replacement, the new device can go through the registration process as described above.

What are the potential limitations or attack vectors of the two-step proving and verification (2PV) procedure, and how can they be further mitigated?

One potential limitation of the 2PV procedure is the computational overhead involved in generating and verifying proofs, especially for large datasets or complex models. This can impact the scalability and efficiency of the system. To mitigate this, optimizations such as using recursive proof systems or specialized hardware for proof generation can be implemented to reduce computational costs.
Attack vectors could include collusion between workers to submit false proofs, replay attacks where workers resubmit accepted proofs, or malicious devices providing fake data. These can be mitigated by implementing strict validation checks in the verification process, ensuring that proofs are unique and valid. Additionally, introducing random challenges during the proving process can help prevent replay attacks.

How can the system be adapted to support more complex federated learning tasks, such as multi-task or hierarchical federated learning, while preserving the end-to-end verifiability properties?

To support more complex federated learning tasks like multi-task or hierarchical federated learning, the system can be extended to accommodate different learning objectives and structures. For multi-task learning, the system can allow workers to contribute to multiple tasks simultaneously by partitioning the data and models accordingly. Hierarchical federated learning can be supported by introducing a hierarchy of workers where higher-level workers aggregate local updates from lower-level workers.
To preserve end-to-end verifiability properties in these scenarios, the system can ensure that proofs are generated and verified at each level of the hierarchy, maintaining transparency and integrity throughout the learning process. Additionally, the verification process can be extended to validate the relationships between tasks or levels in hierarchical learning, ensuring that the overall learning objectives are met accurately and securely.