Balancing Data Privacy and Efficiency in Federated Learning with Social Connections

Core Concepts

The author argues that by leveraging social connections, a novel Social-aware Clustered Federated Learning scheme can enhance model utility without sacrificing privacy. This approach strikes a balance between data privacy and efficiency in federated learning.

Abstract

The content discusses the challenges of preserving data privacy while maintaining efficiency in federated learning. It introduces a novel approach, SCFL, that utilizes social connections to improve model utility without compromising privacy. The paper outlines the design of SCFL in three steps: stable social cluster formation, trust-privacy mapping, and distributed convergence. Experimental results validate the effectiveness of SCFL in enhancing learning utility and protecting user privacy.
Key points:

Federated learning aims to preserve data privacy but faces potential leakage issues.
Differential privacy approaches add noise to computing results but degrade model performance.
SCFL proposes a new scheme using social connections to enhance model utility without sacrificing privacy.
The design includes stable social cluster formation, differentiated trust-privacy mapping, and distributed convergence.
Experiments on real-world datasets show improved learning utility and customizable privacy protection.

Stats

Nearly 75% of data is expected to be processed outside clouds by 2025.
2.93 billion users monthly interact via Facebook.
Experiments validate clients' private training data can be stolen from shared gradients.

Quotes

"SCFL considerably enhances model utility without sacrificing privacy."
"Social ties among users enable the formation of socially clustered federations."
"Users can easily invite trusted friends for cooperative learning within social clusters."

Key Insights Distilled From

Social-Aware Clustered Federated Learning with Customized Privacy Preservation

by Yuntao Wang,... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2212.13992.pdf

Deeper Inquiries

How can the SCFL approach be adapted for different types of FL tasks

The SCFL approach can be adapted for different types of FL tasks by considering the specific requirements and characteristics of each task. Here are some ways to adapt the SCFL approach:

Task-specific trust thresholds: Depending on the sensitivity of the data and privacy concerns in different FL tasks, the trust threshold (αth) can be adjusted accordingly. Tasks that involve highly sensitive information may require a higher trust threshold to ensure adequate privacy protection.

Customized perturbation levels: The personalized LDP perturbations can be tailored based on the nature of the data and participants in each FL task. For tasks with varying levels of data heterogeneity or non-IID distributions, adjusting the noise scale (σ) based on individual preferences and social connections can enhance privacy while maintaining utility.

Adaptive cluster formation: The algorithm for forming stable social clusters can be modified to account for task-specific factors such as communication patterns, user preferences, and historical interactions. By incorporating task-specific criteria into the clustering process, more optimized clusters can be formed for each FL task.

Performance evaluation metrics: Different FL tasks may have unique performance evaluation metrics based on their objectives (e.g., accuracy, convergence rate). Adapting these metrics within the SCFL framework allows for a more targeted optimization strategy tailored to each specific task.

What are the implications of relying on social connections for data privacy in FL

Relying on social connections for data privacy in Federated Learning (FL) has several implications:

Enhanced Privacy Protection: Leveraging social connections allows users to form trusted clusters where they can share raw model updates without adding significant noise, thus enhancing privacy protection compared to traditional differential privacy approaches.

Improved Model Utility: By aggregating model updates within socially connected clusters, users can maintain better model utility as they do not need to add excessive noise that could degrade performance.

Selective Data Sharing: Users have control over which socially connected individuals they collaborate with in a cluster, enabling selective sharing of information only with trusted peers while keeping other participants' data private.

Dynamic Privacy Levels: Personalized LDP perturbations based on social trust degrees enable users to adjust their privacy protection levels according to their relationships with other cluster members, offering dynamic and customizable privacy measures.

5Trustworthiness Concerns: Relying solely on social connections for data privacy raises concerns about potential breaches if adversaries infiltrate trusted clusters or exploit vulnerabilities in interpersonal relationships.

How might incorporating personalized LDP perturbations impact overall system performance

Incorporating personalized Local Differential Privacy (LDP) perturbations into Federated Learning systems has both benefits and challenges that impact overall system performance:
Benefits:

Enhanced Privacy: Personalized LDP perturbations allow users to tailor their level of noise addition based on individual preferences and trust levels.
Improved User Satisfaction: Users feel more empowered when given control over their own privacy settings.
Optimized Tradeoff between Privacy & Utility: Customizing LDP perturbations helps strike a balance between preserving user privacy while maintaining acceptable model performance.
Challenges:

Complexity: Implementing personalized LDP requires additional computational resources and algorithms tailored for individual needs.
Privacy Risks: If not implemented correctly or if users misjudge their own security needs, there is a risk of exposing sensitive information despite customized protections.
System Overhead: Managing multiple levels of personalization adds complexity to system design and maintenance processes.
Overall System Performance:
Personalized LDP perturbations impact overall system performance by influencing tradeoffs between user satisfaction through enhanced customization options versus increased complexity from managing diverse settings across various participants' needs