Lotto: Secure Participant Selection in Federated Learning Against Adversarial Servers

核心概念

Lotto provides secure participant selection in Federated Learning to prevent manipulation by adversarial servers.

摘要

Lotto addresses the issue of secure participant selection in Federated Learning to prevent adversarial servers from manipulating client selection. It introduces random and informed selection algorithms, ensuring fairness and security. By incorporating verifiable randomness and population refinement, Lotto aligns the proportion of compromised participants with the base rate of dishonest clients. The protocol guarantees consistency and security throughout the training process, maintaining privacy while maximizing utility.

統計資料

SecAgg safeguards a participant i’s plaintext update xxxi from server probing by distributing secret shares of sk1i and bi among other participants.
Distributed DP ensures that no specific client’s participation significantly increases the likelihood of any observed aggregated update by potential adversaries.

引述

從以下內容提煉的關鍵洞見

Lotto

by Zhifeng Jian... 於 arxiv.org 03-07-2024

https://arxiv.org/pdf/2401.02880.pdf

深入探究

How does Lotto's approach to secure participant selection impact the overall efficiency of Federated Learning systems

Lotto's approach to secure participant selection significantly impacts the overall efficiency of Federated Learning systems in a positive manner. By incorporating verifiable randomness and secure selection algorithms, Lotto ensures that the proportion of compromised participants aligns with the base rate of dishonest clients in the population. This alignment enhances the security of FL systems by preventing malicious servers from manipulating client selection processes to include more adversaries.
In terms of efficiency, Lotto's approach adds a layer of security without compromising performance. The use of verifiable randomness allows each client to autonomously determine their participation, reducing reliance on centralized control and potential vulnerabilities. Additionally, by employing over-selection with controlled residual removal for informed selection algorithms, Lotto strikes a balance between privacy and utility while maintaining robust security measures.
Overall, Lotto's methodology not only enhances the security posture of Federated Learning systems but also contributes to improved efficiency by ensuring fair participant selection without sacrificing performance.

What potential drawbacks or limitations might arise from relying on verifiable randomness for client selection

While relying on verifiable randomness for client selection offers significant advantages in enhancing security and fairness in participant selection processes, there are potential drawbacks or limitations that should be considered:

Complexity: Implementing verifiable randomness mechanisms can introduce additional complexity to FL systems. Clients need to generate random values using cryptographic primitives like VRFs accurately, which may require specialized knowledge or resources.

Computational Overhead: Verifying randomness proofs and conducting consistency checks can add computational overhead to the system, potentially impacting performance during training rounds.

Privacy Concerns: While verifiable randomness helps ensure fair participant selection, it may inadvertently reveal information about clients' behavior or decision-making processes if not implemented carefully.

Scalability Issues: As FL systems scale up with larger populations or more complex algorithms, managing verifiable randomness for all participants could become challenging and resource-intensive.

It is essential for developers implementing such mechanisms to address these drawbacks effectively through careful design considerations and optimizations to mitigate any negative impact on system performance or user experience.

How could the principles of secure participant selection in Federated Learning be applied to other collaborative machine learning scenarios

The principles of secure participant selection in Federated Learning can be applied beyond this specific scenario to other collaborative machine learning contexts where multiple parties contribute data for model training while preserving privacy:

Multi-Party Computation (MPC): Secure techniques like those used in Lotto can be adapted for MPC scenarios where multiple entities collaborate on computations without revealing individual inputs.

Decentralized Machine Learning Platforms: Platforms that enable decentralized model training across various devices could benefit from secure participant selection methods similar to those employed by Lotto.

Cross-Institutional Collaborations: In collaborative settings involving different institutions sharing data for research purposes, ensuring fair and secure participant selection is crucial; applying concepts from Federated Learning can enhance trust among stakeholders.

By leveraging the principles established in securing participant selections within Federated Learning frameworks like Lotto, other collaborative machine learning scenarios can uphold data privacy standards while promoting efficient collaboration among diverse entities involved in model development initiatives.