Differentially Private Online Federated Learning with Correlated Noise

Core Concepts

Proposing a novel differentially private algorithm for online federated learning using correlated noise to enhance utility while ensuring privacy.

Abstract

The content introduces a novel approach to online federated learning with differential privacy and correlated noise. It addresses challenges of DP noise and local updates with streaming non-iid data, focusing on utility improvement while maintaining privacy. The proposed algorithm is validated through numerical experiments. The paper discusses the importance of real-time decision-making applications like hospital networks and outlines the relevance of OFL in various domains. Privacy concerns in collaborative learning are highlighted, emphasizing the need for DP techniques. The work extends temporally correlated DP noise mechanisms to OFL scenarios, demonstrating efficacy through perturbed iterate analysis and quasi-strong convexity conditions.

I. Introduction

• Focus on online federated learning (OFL) merging FL principles with online optimization.
• Relevance of OFL in real-time decision-making applications.
• Challenges of streaming non-iid data and privacy concerns in collaborative learning.

II. Problem Formulation

• Description of OFL architecture with server coordinating learners.
• Definition of dynamic regret metrics for utility quantification.
• Privacy threat model considerations for adaptive continuous release.

III. Algorithm

• Proposed DP algorithm utilizing correlated noise and local updates.
• Matrix factorization technique for privacy protection.
• Analysis of utility and privacy trade-offs.

IV. Analysis

• Assumptions on loss functions for regret bounds.
• Utility analysis using perturbed iterate technique.
• Privacy analysis based on matrix factorization strategies.

V. Experiments

• Impact assessment of local update frequency (τ) on algorithm performance.
• Comparison under different DP budgets with existing algorithms.

Stats

"Subject to an (ϵ, δ)-DP budget, we establish a dynamic regret bound over the entire time horizon that quantifies the impact of key parameters and the intensity of changes in dynamic environments."

Quotes

"The majority of research on differentially private FL studies an offline setting and adds a privacy-preserving noise that is independent across iterations."
"Some works explore differentially private OFL algorithms with independent noise, but none consider adaptive continuous release."

Key Insights Distilled From

Differentially Private Online Federated Learning with Correlated Noise

by Jiaojiao Zha... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16542.pdf

Deeper Inquiries

How can the proposed algorithm be adapted for scenarios where the server is not trusted

To adapt the proposed algorithm for scenarios where the server is not trusted, additional measures need to be implemented to ensure privacy and security. One approach could involve introducing secure multi-party computation techniques to distribute the trust among multiple entities without compromising data confidentiality. This would involve encrypting sensitive information before sharing it with any party involved in the federated learning process. By utilizing cryptographic protocols such as homomorphic encryption or secure enclaves, computations can be performed on encrypted data without exposing it to any single entity.
Another strategy could involve incorporating a decentralized architecture where each participant retains control over their data and model updates. This way, no central server has access to all information, reducing the risk of privacy breaches. Secure communication channels using techniques like secure sockets layer (SSL) or virtual private networks (VPNs) can also enhance data protection during transmission between participants.
By implementing these strategies, the algorithm can maintain its differential privacy guarantees while ensuring that no single entity poses a threat to the overall security and privacy of the collaborative learning process.

What are the implications of using temporally correlated noise in improving utility while maintaining privacy

The use of temporally correlated noise in improving utility while maintaining privacy offers several advantages in online federated learning scenarios. Firstly, by leveraging correlated noise processes through matrix factorization techniques, it becomes possible to introduce structured perturbations that preserve more useful information within the released models compared to independent noise injection methods. This leads to improved convergence rates and reduced loss error over time.
Furthermore, temporally correlated noise allows for better trade-offs between utility and privacy by controlling how much perturbation is added at each step based on historical context rather than random fluctuations seen with independent noise addition. This results in more efficient utilization of differential privacy budgets while still achieving desired levels of protection against inference attacks.
Overall, integrating temporally correlated noise into online federated learning algorithms enhances both performance metrics and ensures robust privacy preservation throughout continuous model updates.

How does the concept of differential privacy impact collaborative learning beyond the scope of this article

The concept of differential privacy has far-reaching implications beyond collaborative learning applications discussed in this article. In various domains such as healthcare, finance, social media platforms, and government services where sensitive user data is collected and analyzed for decision-making purposes, ensuring individual privacy remains paramount.
Differential Privacy provides a rigorous framework for quantifying how much an individual's personal information contributes to an analysis without revealing specific details about that individual's data points or identity. By incorporating differential privacy mechanisms into algorithms used across different sectors—such as recommendation systems safeguarding user preferences or medical research protecting patient records—organizations can uphold ethical standards while deriving valuable insights from aggregated datasets.
Moreover, Differential Privacy fosters trust between users/consumers and organizations handling their data by demonstrating a commitment towards preserving confidentiality even when performing complex analyses or machine learning tasks on sensitive information sets.