insight - Computer Security and Privacy - # Privacy-Preserving Federated Learning

Immersion and Invariance-based Coding for Preserving Privacy in Federated Learning

Q: How can the proposed framework be extended to handle heterogeneous client data distributions and non-i.i.d. data in federated learning settings?

The proposed framework can be extended to handle heterogeneous client data distributions and non-i.i.d. data by incorporating adaptive mechanisms that account for the variability in data across clients. One approach is to modify the target optimization algorithm to include weighting schemes that reflect the distribution of data at each client. This can be achieved by adjusting the aggregation process to consider the local data characteristics, such as the size and distribution of the datasets held by each client. Additionally, the framework can integrate techniques like personalized federated learning, where each client maintains a local model that is fine-tuned based on its unique data distribution. This can be facilitated by introducing meta-learning strategies that allow the global model to adapt to the diverse local models while still preserving privacy through the immersion-based coding scheme. Moreover, the encoding maps can be designed to incorporate information about the local data distributions, allowing the framework to maintain performance and convergence rates even in the presence of non-i.i.d. data. By leveraging these adaptive strategies, the proposed privacy-preserving federated learning framework can effectively manage the challenges posed by heterogeneous client data distributions.

Q: What are the potential limitations or drawbacks of the immersion-based coding approach compared to other privacy-preserving techniques like differential privacy or secure multi-party computation?

While the immersion-based coding approach offers several advantages, such as maintaining model performance and providing strong privacy guarantees, it also has potential limitations. One drawback is the complexity of designing the immersion maps and target optimizers, which may require significant expertise in control theory and system dynamics. This complexity could hinder the practical implementation of the framework in real-world applications. Additionally, the reliance on random coding introduces a level of randomness that, while beneficial for privacy, may complicate the reproducibility of results. In contrast, techniques like differential privacy provide a more straightforward framework for quantifying privacy guarantees through the addition of noise, making them easier to implement and understand. Furthermore, while secure multi-party computation (MPC) ensures that no party can access the raw data, it may incur higher communication and computational costs compared to the immersion-based approach. The latter, while efficient, may still be vulnerable to certain types of inference attacks if the encoding maps are not designed robustly enough. Lastly, the immersion-based coding approach may not be as widely studied or accepted as differential privacy or MPC, which could limit its adoption in the federated learning community. Therefore, while it presents a promising avenue for privacy preservation, careful consideration of these limitations is essential for its successful application.

Q: Can the system immersion and random coding ideas be applied to other machine learning tasks beyond federated learning, such as distributed training of deep neural networks or privacy-preserving inference?

Yes, the concepts of system immersion and random coding can be effectively applied to other machine learning tasks beyond federated learning, including distributed training of deep neural networks and privacy-preserving inference. In distributed training scenarios, the immersion-based coding framework can facilitate the secure aggregation of model updates from multiple nodes while ensuring that individual data privacy is maintained. By treating the optimization algorithms as dynamical systems, similar immersion techniques can be employed to encode model parameters, allowing for efficient training without exposing sensitive data. In the context of privacy-preserving inference, the random coding ideas can be utilized to obfuscate the model parameters during the inference process, ensuring that the outputs do not reveal sensitive information about the training data. This can be particularly useful in applications such as healthcare, where patient data privacy is paramount. Moreover, the flexibility of the immersion-based coding approach allows it to be adapted for various machine learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By designing appropriate encoding and decoding mechanisms, the framework can maintain the integrity and performance of these models while providing robust privacy guarantees. Overall, the principles of system immersion and random coding offer a versatile toolkit for enhancing privacy across a wide range of machine learning applications, making them valuable for future research and development in the field.

Core Concepts

A privacy-preserving federated learning framework is proposed that uses random coding and system immersion tools to protect the privacy of local and global models without compromising model performance or system efficiency.

Abstract

The content presents a novel privacy-preserving federated learning (PPFL) framework that leverages random coding and system immersion tools from control theory. The key idea is to treat the optimization algorithms used in standard federated learning (FL) schemes as dynamical systems and immerse them into a higher-dimensional "target" optimization algorithm.

The framework consists of the following steps:

Server Encoding: The server encodes the global model parameters using an affine immersion map π1(·) before broadcasting to clients. This creates a higher-dimensional representation of the model parameters.
Client Local Training: Clients update their local models using a target optimization algorithm designed to work on the encoded parameters. This target algorithm is designed to converge to an encoded version of the true local model parameters.
Aggregation: Clients send their encoded local model updates to a third-party aggregator, who aggregates them and sends the encoded aggregated model to the server.
Server Decoding: The server decodes the aggregated model using the left inverse of the encoding map π1(·) to retrieve the original aggregated model.

An additional encoding step by the aggregator using π2(·) is introduced to further protect the privacy of the intermediate global models from the server.

The proposed framework is shown to provide any desired level of differential privacy guarantee for both local and global models without compromising the accuracy and convergence rate of the federated learning algorithm. It also maintains computational efficiency compared to other privacy-preserving approaches like differential privacy, secure multi-party computation, and homomorphic encryption.

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The content does not provide any specific numerical data or metrics. It focuses on the conceptual design of the privacy-preserving federated learning framework.

Quotes

"The core idea involves treating the Gradient descent optimization algorithms, e.g., SGD, Adam, Momentum, etc., commonly used in standard Federated Learning, as a dynamical system that we seek to immerse into a higher-dimensional algorithm (the so-called target optimization algorithm)."
"We demonstrate that the proposed privacy-preserving scheme can be tailored to offer any desired level of differential privacy for local and global model parameters while maintaining the same accuracy and convergence rate as standard FL algorithms."

Key Insights Distilled From

Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning

by Haleh Hayati... at arxiv.org 09-27-2024

https://arxiv.org/pdf/2409.17201.pdf

Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning

Deeper Inquiries

How can the proposed framework be extended to handle heterogeneous client data distributions and non-i.i.d. data in federated learning settings?

The proposed framework can be extended to handle heterogeneous client data distributions and non-i.i.d. data by incorporating adaptive mechanisms that account for the variability in data across clients. One approach is to modify the target optimization algorithm to include weighting schemes that reflect the distribution of data at each client. This can be achieved by adjusting the aggregation process to consider the local data characteristics, such as the size and distribution of the datasets held by each client.
Additionally, the framework can integrate techniques like personalized federated learning, where each client maintains a local model that is fine-tuned based on its unique data distribution. This can be facilitated by introducing meta-learning strategies that allow the global model to adapt to the diverse local models while still preserving privacy through the immersion-based coding scheme.
Moreover, the encoding maps can be designed to incorporate information about the local data distributions, allowing the framework to maintain performance and convergence rates even in the presence of non-i.i.d. data. By leveraging these adaptive strategies, the proposed privacy-preserving federated learning framework can effectively manage the challenges posed by heterogeneous client data distributions.

What are the potential limitations or drawbacks of the immersion-based coding approach compared to other privacy-preserving techniques like differential privacy or secure multi-party computation?

While the immersion-based coding approach offers several advantages, such as maintaining model performance and providing strong privacy guarantees, it also has potential limitations. One drawback is the complexity of designing the immersion maps and target optimizers, which may require significant expertise in control theory and system dynamics. This complexity could hinder the practical implementation of the framework in real-world applications.
Additionally, the reliance on random coding introduces a level of randomness that, while beneficial for privacy, may complicate the reproducibility of results. In contrast, techniques like differential privacy provide a more straightforward framework for quantifying privacy guarantees through the addition of noise, making them easier to implement and understand.
Furthermore, while secure multi-party computation (MPC) ensures that no party can access the raw data, it may incur higher communication and computational costs compared to the immersion-based approach. The latter, while efficient, may still be vulnerable to certain types of inference attacks if the encoding maps are not designed robustly enough.
Lastly, the immersion-based coding approach may not be as widely studied or accepted as differential privacy or MPC, which could limit its adoption in the federated learning community. Therefore, while it presents a promising avenue for privacy preservation, careful consideration of these limitations is essential for its successful application.

Can the system immersion and random coding ideas be applied to other machine learning tasks beyond federated learning, such as distributed training of deep neural networks or privacy-preserving inference?

Yes, the concepts of system immersion and random coding can be effectively applied to other machine learning tasks beyond federated learning, including distributed training of deep neural networks and privacy-preserving inference. In distributed training scenarios, the immersion-based coding framework can facilitate the secure aggregation of model updates from multiple nodes while ensuring that individual data privacy is maintained. By treating the optimization algorithms as dynamical systems, similar immersion techniques can be employed to encode model parameters, allowing for efficient training without exposing sensitive data.
In the context of privacy-preserving inference, the random coding ideas can be utilized to obfuscate the model parameters during the inference process, ensuring that the outputs do not reveal sensitive information about the training data. This can be particularly useful in applications such as healthcare, where patient data privacy is paramount.
Moreover, the flexibility of the immersion-based coding approach allows it to be adapted for various machine learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By designing appropriate encoding and decoding mechanisms, the framework can maintain the integrity and performance of these models while providing robust privacy guarantees.
Overall, the principles of system immersion and random coding offer a versatile toolkit for enhancing privacy across a wide range of machine learning applications, making them valuable for future research and development in the field.