Preserving Privacy in Federated Learning through Differential Privacy

Core Concepts

Federated learning and differential privacy can be combined to enable large-scale machine learning over distributed datasets while providing rigorous privacy guarantees.

Abstract

The article discusses the benefits of combining federated learning (FL) and differential privacy (DP) to enable privacy-preserving large-scale machine learning.
FL allows multiple remote clients to collaboratively train a machine learning model without exposing their raw data, by sharing only model updates. However, FL alone does not provide formal privacy guarantees, as the model updates can still leak private information. DP, on the other hand, provides a rigorous mathematical framework to limit the privacy leakage from the model outputs.
The article first introduces the key concepts of FL and DP, and highlights how their combination can address the conflict between data-hungry machine learning and growing privacy concerns. It then reviews the current research advances in integrating DP into FL, categorizing the different paradigms and notions, such as centralized DP, local DP, and distributed DP.
To achieve usable FL with DP, the article presents high-level optimization principles from the perspectives of DP and FL. DP-focused optimizations include improving gradient clipping, noise distribution, and privacy loss composition. FL-focused optimizations leverage the characteristics of massive FL clients and sparse model parameters, such as reducing update frequency, compressing model parameters, and sampling participating clients.
Finally, the article discusses future challenges in applying FL with DP to emerging areas like vertical/transfer federation, large language models, and streaming data, as well as considerations around robustness, fairness, and the "right to be forgotten".

Stats

"Federated learning has great potential for large-scale machine learning (ML) without exposing raw data."
"Differential privacy is the de facto standard of privacy protection with provable guarantees."
"The advancement of FL in privacy protection stems from the delicacy in restricting raw data sharing."
"DP algorithms hide the presence of any individual sample or client by adding noise to model parameters, also leading to possible utility loss."

Quotes

"Aimed at exploiting the potential of ML to its fullest, it is highly desirable and essential to build FL with DP to train and refine ML models with more comprehensive datasets."
"Utility optimization, i.e., improving the model utility as much utility as possible for a given privacy guarantee is an essential problem in the combining use of FL and DP."
"Speculatively, the combination of FL and DP can significantly extend the applicable areas for both techniques and bring privacy-preserving large-scale ML to reality."

Key Insights Distilled From

Belt and Brace: When Federated Learning Meets Differential Privacy

by Xuebin Ren,S... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18814.pdf

Deeper Inquiries

How can the integration of FL and DP be extended to support emerging applications like vertical/transfer federation and large language models while preserving privacy and utility?

The integration of Federated Learning (FL) and Differential Privacy (DP) can be extended to support emerging applications like vertical/transfer federation and large language models by implementing tailored solutions that address the specific challenges of these applications while ensuring both privacy and utility.
For vertical/transfer federation, where each party holds different features of the same set of samples, DP can be applied to safeguard Vertical Federated Learning (VFL). This involves adapting DP mechanisms to protect the privacy of data exchanged between parties with non-overlapping features. Techniques such as secure shuffling and subsampling can be utilized to ensure privacy while maintaining utility in this context.
In the case of large language models, which often have billions of parameters, the integration of FL and DP needs to consider the scalability and complexity of these models. Strategies such as noise distribution optimization, model parameters compression, and adaptive gradient clipping can help reduce noise variance and communication overhead while preserving privacy. Additionally, exploring intrinsic DP computation, leveraging the inherent randomness of models, can provide noise-free DP for large language models.
Overall, extending the integration of FL and DP for these emerging applications requires a nuanced approach that balances the unique requirements of each application with the principles of privacy protection and utility optimization.

How can the trade-offs between privacy, fairness, and robustness be balanced in the design of DP-enhanced FL systems?

Balancing the trade-offs between privacy, fairness, and robustness in the design of DP-enhanced FL systems requires careful consideration of the interplay between these factors.

Privacy: DP mechanisms should be implemented to ensure that sensitive data is protected during the FL process. Techniques like differential privacy amplification, noise distribution optimization, and privacy loss composition can help achieve the desired level of privacy while minimizing the impact on utility.

Fairness: To address fairness concerns, FL systems should incorporate mechanisms to mitigate bias and ensure equitable treatment of all participants. This may involve data preprocessing techniques to address bias in the training data and model evaluation metrics that account for fairness considerations.

Robustness: Ensuring the robustness of FL systems involves safeguarding against failures, attacks, and unexpected events that may compromise the integrity of the system. Robust aggregation protocols, anomaly detection mechanisms, and secure communication channels can enhance the resilience of FL systems to various threats.

By integrating privacy-preserving techniques with fairness-aware algorithms and robust security measures, DP-enhanced FL systems can strike a balance between privacy protection, fairness, and robustness, thereby promoting trust and reliability in the system.

What new privacy notions and mechanisms are needed to enable the "right to be forgotten" in the context of federated unlearning?

Enabling the "right to be forgotten" in the context of federated unlearning requires the development of new privacy notions and mechanisms that address the unique challenges of removing individual data from FL systems.

Federated Unlearning Mechanisms: Specific mechanisms need to be designed to facilitate the removal of individual data from FL models without compromising the overall model performance. This may involve recording historical parameter updates, implementing unlearning algorithms, and ensuring that the removal process is efficient and effective.

Privacy-Preserving Techniques: New privacy-preserving techniques, such as secure deletion protocols, cryptographic methods for data obfuscation, and data anonymization strategies, can be employed to ensure that the forgotten data is irretrievable and does not leave any trace in the FL system.

Compliance with Data Regulations: The development of privacy notions that align with data regulations, such as the General Data Protection Regulation (GDPR), is essential to ensure that the "right to be forgotten" is implemented in a legally compliant manner within FL systems.

By incorporating these new privacy notions and mechanisms, federated unlearning can effectively support the "right to be forgotten" by allowing individuals to request the removal of their data from FL models while upholding privacy and data protection principles.