A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
Trustworthy Federated Learning (TFL) typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency reduction while protecting data privacy. Therefore, protection mechanisms and their parameters should be carefully chosen to strike an optimal tradeoff between \textit{privacy leakage}, \textit{utility loss}, and \textit{efficiency reduction}. To this end, federated learning practitioners need tools to measure the three factors and optimize the tradeoff between them to choose the protection mechanism that is most appropriate to the application at hand. Motivated by this requirement, we propose a framework that (1) formulates TFL as a problem of finding a protection mechanism to optimize the tradeoff between privacy leakage, utility loss, and efficiency reduction and (2) formally defines bounded measurements of the three factors. We then propose a meta-learning algorithm to approximate this optimization problem and find optimal protection parameters for representative protection mechanisms, including Randomization, Homomorphic Encryption, Secret Sharing, and Compression. We further design estimation algorithms to quantify these found optimal protection parameters in a practical horizontal federated learning setting and provide a theoretical analysis of the estimation error.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningMeta-LearningSimilar Papers 제목 키워드 기반
MetaDE: Evolving Differential Evolution by Differential Evolution
As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE …
Computational EfficiencyGPUHyperparameter OptimizationAI-based Blackbox Code Deobfuscation: Understand, Improve and Mitigate
Code obfuscation aims at protecting Intellectual Property and other secrets embedded into software from being retrieved. Recent works leverage advances in artificial intelligence with the hope of getting blackbox deobfus…
Tail-risk protection: Machine Learning meets modern Econometrics
Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms …
BIG-bench Machine LearningEconometricsTrading Off Privacy, Utility and Efficiency in Federated Learning
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulf…
Federated LearningVertical Federated LearningA Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), has recently gained increasing attention a…
Federated LearningPrivacy PreservingVertical Federated Learning