innsikt - Machine Learning - # Differentially Private Federated Learning

Differentially Private Federated Learning Mechanism using Correlated Binary Stochastic Quantization

Q: How can the proposed correlated quantization techniques be extended to larger output alphabets beyond binary?

The proposed correlated quantization techniques, specifically the CorBin-FL and AugCorBin-FL mechanisms, can be extended to larger output alphabets by generalizing the quantization process to accommodate multiple discrete values instead of just binary outputs. This can be achieved through the following steps: Multi-Class Quantization: Instead of mapping inputs to two outputs (e.g., -1 and 1), the quantization function can be designed to map inputs to a set of discrete values, say ( { \gamma_1, \gamma_2, \ldots, \gamma_k } ). This requires redefining the quantization algorithm to handle ( k ) classes, where ( k > 2 ). Thresholding and Ties: The thresholding mechanism used in binary quantization can be adapted to create multiple thresholds that determine which output class to select based on the input value. For instance, if the input falls within certain ranges, it can be mapped to different output classes. The tie-breaking mechanism can also be modified to incorporate additional random variables to ensure that the outputs remain unbiased and satisfy the differential privacy constraints. Correlated Noise Generation: The generation of correlated noise can be extended to multiple outputs by ensuring that the noise added to each quantizer is still correlated but now reflects the multi-class nature of the outputs. This can involve using a shared source of randomness that is capable of producing values suitable for the larger output space. Privacy Guarantees: The privacy guarantees must be re-evaluated to ensure that the new multi-class quantization still satisfies the desired differential privacy measures (e.g., PLDP, UCDP, SCDP). This may involve deriving new bounds and conditions that account for the increased complexity of the output space. By implementing these strategies, the correlated quantization techniques can effectively handle larger output alphabets, thereby enhancing their applicability in various machine learning scenarios.

Q: How can the correlated quantization be generalized to groups of more than two clients?

Generalizing correlated quantization to groups of more than two clients involves several key modifications to the existing framework: Group Formation: Instead of pairing clients, the mechanism can be designed to form groups of ( g ) clients, where ( g > 2 ). Each group can share a common source of randomness, which is essential for maintaining the correlation among the quantized outputs. Multi-Output Quantization: The quantization algorithm must be adapted to handle multiple inputs simultaneously. This can be achieved by designing a multi-dimensional quantizer that takes inputs from all clients in the group and produces a correlated output for each client. The output can be structured as a vector, where each element corresponds to a client's quantized value. Shared Randomness: The mechanism for generating shared random bits must be extended to accommodate the larger group size. This can involve using a more complex protocol for distributing the common randomness among all group members, ensuring that the randomness remains secure and private. Aggregation and Privacy Analysis: The aggregation of the quantized outputs from multiple clients must be carefully designed to ensure that the overall privacy guarantees are maintained. This may require deriving new privacy bounds that account for the increased number of clients and the interactions between their outputs. Utility Optimization: The utility of the quantization process must be evaluated in the context of group dynamics. This involves analyzing how the correlation among multiple clients affects the mean square error (MSE) and ensuring that the quantization remains unbiased while optimizing the privacy-utility trade-off. By implementing these modifications, the correlated quantization techniques can be effectively generalized to accommodate groups of more than two clients, thereby enhancing the scalability and flexibility of the federated learning framework.

Grunnleggende konsepter

The CorBin-FL and AugCorBin-FL mechanisms achieve differential privacy guarantees in federated learning by using correlated binary stochastic quantization of local model updates.

Sammendrag

The content introduces two novel privacy mechanisms for federated learning:

CorBin-FL:

Uses correlated binary stochastic quantization to achieve parameter-level local differential privacy (PLDP).
Clients share a limited amount of common randomness to perform the correlated quantization without compromising individual privacy.
Provides theoretical analysis showing CorBin-FL asymptotically optimizes the privacy-utility tradeoff between mean squared error and PLDP.

AugCorBin-FL:

An extension of CorBin-FL that, in addition to PLDP, also achieves user-level and sample-level central differential privacy.
A hybrid mechanism where a fraction of clients use CorBin-FL and the rest use the LDP-FL mechanism.
Provides bounds on the privacy parameters and mean squared error performance.

The proposed mechanisms are shown to outperform existing differentially private federated learning approaches, including the Gaussian, Laplacian, and LDP-FL mechanisms, in terms of model accuracy under equal PLDP privacy budgets. The mechanisms are also robust to client dropouts and scale well with the number of clients.

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Statistikk

The mean square error of the CorBin-FL mechanism is bounded by:
r^2/(2n)((sqrt(2)-1)α(ϵ_p)+1)/((sqrt(2)+1)α(ϵ_p)-1)
The mean square error of the AugCorBin-FL mechanism is bounded by:
γr^2α^2(ϵ_p)/n + (1-γ)2r^2/(nθ)((sqrt(2)-1)α(ϵ_p)+1)/((sqrt(2)+1)α(ϵ_p)-1)

Sitater

"The CorBin-FL mechanism is unbiased, i.e., E(W_g) = 1/n Σ_i∈[n] w_i, where w_i, i ∈[n] are the local client updates, and W_g is the average of the obfuscated updates at the server."
"The AugCorBin-FL mechanism achieves (ϵ_u, δ)-UCDP and ϵ_p-PLDP."

Viktige innsikter hentet fra

CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness

by Hojat Allah ... klokken arxiv.org 09-23-2024

https://arxiv.org/pdf/2409.13133.pdf

CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness

Dypere Spørsmål

How can the proposed correlated quantization techniques be extended to larger output alphabets beyond binary?

The proposed correlated quantization techniques, specifically the CorBin-FL and AugCorBin-FL mechanisms, can be extended to larger output alphabets by generalizing the quantization process to accommodate multiple discrete values instead of just binary outputs. This can be achieved through the following steps:

Multi-Class Quantization: Instead of mapping inputs to two outputs (e.g., -1 and 1), the quantization function can be designed to map inputs to a set of discrete values, say ( { \gamma_1, \gamma_2, \ldots, \gamma_k } ). This requires redefining the quantization algorithm to handle ( k ) classes, where ( k > 2 ).

Thresholding and Ties: The thresholding mechanism used in binary quantization can be adapted to create multiple thresholds that determine which output class to select based on the input value. For instance, if the input falls within certain ranges, it can be mapped to different output classes. The tie-breaking mechanism can also be modified to incorporate additional random variables to ensure that the outputs remain unbiased and satisfy the differential privacy constraints.

Correlated Noise Generation: The generation of correlated noise can be extended to multiple outputs by ensuring that the noise added to each quantizer is still correlated but now reflects the multi-class nature of the outputs. This can involve using a shared source of randomness that is capable of producing values suitable for the larger output space.

Privacy Guarantees: The privacy guarantees must be re-evaluated to ensure that the new multi-class quantization still satisfies the desired differential privacy measures (e.g., PLDP, UCDP, SCDP). This may involve deriving new bounds and conditions that account for the increased complexity of the output space.

By implementing these strategies, the correlated quantization techniques can effectively handle larger output alphabets, thereby enhancing their applicability in various machine learning scenarios.

How can the correlated quantization be generalized to groups of more than two clients?

Generalizing correlated quantization to groups of more than two clients involves several key modifications to the existing framework:

Group Formation: Instead of pairing clients, the mechanism can be designed to form groups of ( g ) clients, where ( g > 2 ). Each group can share a common source of randomness, which is essential for maintaining the correlation among the quantized outputs.

Multi-Output Quantization: The quantization algorithm must be adapted to handle multiple inputs simultaneously. This can be achieved by designing a multi-dimensional quantizer that takes inputs from all clients in the group and produces a correlated output for each client. The output can be structured as a vector, where each element corresponds to a client's quantized value.

Shared Randomness: The mechanism for generating shared random bits must be extended to accommodate the larger group size. This can involve using a more complex protocol for distributing the common randomness among all group members, ensuring that the randomness remains secure and private.

Aggregation and Privacy Analysis: The aggregation of the quantized outputs from multiple clients must be carefully designed to ensure that the overall privacy guarantees are maintained. This may require deriving new privacy bounds that account for the increased number of clients and the interactions between their outputs.

Utility Optimization: The utility of the quantization process must be evaluated in the context of group dynamics. This involves analyzing how the correlation among multiple clients affects the mean square error (MSE) and ensuring that the quantization remains unbiased while optimizing the privacy-utility trade-off.

By implementing these modifications, the correlated quantization techniques can be effectively generalized to accommodate groups of more than two clients, thereby enhancing the scalability and flexibility of the federated learning framework.

What are the potential applications of the differentially private federated learning mechanisms beyond the image classification tasks considered in this work?

Differentially private federated learning mechanisms, such as CorBin-FL and AugCorBin-FL, have a wide range of potential applications beyond image classification tasks. Some notable applications include:

Healthcare Data Analysis: Federated learning can be applied to train models on sensitive healthcare data from multiple institutions without sharing patient data. This is particularly useful for tasks such as disease prediction, patient outcome forecasting, and personalized treatment recommendations while ensuring patient privacy through differential privacy.

Financial Services: In the finance sector, federated learning can be utilized for fraud detection, credit scoring, and risk assessment. By allowing banks and financial institutions to collaboratively train models on their customer data without exposing sensitive information, these mechanisms can enhance security and compliance with regulations like GDPR.

Natural Language Processing (NLP): Federated learning can be employed in NLP tasks such as language modeling, sentiment analysis, and personalized recommendations. By training models on user-generated text data while preserving privacy, organizations can improve their services without compromising user confidentiality.

Smart Devices and IoT: In the context of Internet of Things (IoT), federated learning can enable smart devices to learn from user interactions and environmental data while maintaining privacy. Applications include predictive maintenance, anomaly detection, and personalized user experiences across devices.

Autonomous Vehicles: Federated learning can facilitate the training of models for autonomous driving systems by allowing vehicles to learn from their experiences on the road without sharing sensitive data about their surroundings or passengers. This can enhance safety and performance while ensuring data privacy.

Recommendation Systems: Federated learning can be applied to build personalized recommendation systems for e-commerce and content platforms. By training models on user behavior data across different platforms while preserving privacy, organizations can provide tailored recommendations without compromising user data.

Smart Grid and Energy Management: In energy management systems, federated learning can be used to optimize energy consumption and distribution by analyzing data from multiple sources, such as smart meters and renewable energy sources, while ensuring that sensitive usage data remains private.

These applications highlight the versatility and potential of differentially private federated learning mechanisms in various domains, enabling organizations to leverage collaborative learning while safeguarding user privacy.