Enhancing Privacy in Decentralized Learning through Virtual Nodes and Randomized Model Sharing

Core Concepts

SHATTER, a novel decentralized learning approach, enhances privacy by having nodes operate multiple virtual nodes that communicate random model chunks through a dynamically changing communication topology, preventing attackers from collecting full models or identifying the original node behind a given model chunk.

Abstract

The content presents SHATTER, a novel privacy-preserving decentralized learning (DL) approach. In standard DL, nodes directly exchange their full models with neighbors, which can lead to privacy breaches through attacks like membership inference, attribute inference, and gradient inversion. SHATTER addresses this by having each real node (RN) operate multiple virtual nodes (VNs). RNs split their local models into chunks and forward these chunks to their VNs. The VNs then communicate the chunks through a dynamically changing communication topology, hiding the identity of the original RN. This approach significantly raises the bar against privacy attacks, as attackers can only access partial model information. The authors provide a comprehensive theoretical analysis, proving the convergence of SHATTER and formally demonstrating its privacy guarantees. They show that as the number of VNs increases, the likelihood of sharing full models and the expected number of exchanged model parameters between RNs decrease, reducing the efficacy of attacks exploiting shared model parameters or gradients. The experimental evaluation compares SHATTER against standard DL and a noise-based privacy-preserving approach (MUFFLIATO). The results show that SHATTER not only renders standard privacy attacks infeasible but also exhibits a positive impact on model convergence, all while incurring a manageable increase in communication volume.

Stats

"The number of model parameters held by each VN is the same (i.e., d = kc for some c ∈N and the chunks of the models assigned to each VN partitions the model of the corresponding RN into k equal sizes of c)." "The kn total VNs collectively form an r-regular dynamic topology, for some 1 ≤r ≤nk−1, facilitating interaction among them for the exchange of the model chunks they individually hold."

Quotes

"SHATTER, a novel DL system that protects the shared model updates from these privacy attacks." "The key idea behind SHATTER is to allow nodes to have direct access only to a model chunk, i.e., a subset of all parameters rather than receiving the full model." "We theoretically prove the convergence of SHATTER and provide a formal analysis demonstrating how SHATTER reduces the efficacy of attacks compared to when exchanging full models between participating nodes."

Key Insights Distilled From

Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes

by Sayan Biswas... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09536.pdf

Deeper Inquiries

How can SHATTER's privacy guarantees be further improved by incorporating additional techniques, such as secure multi-party computation or differential privacy

To further enhance SHATTER's privacy guarantees, incorporating additional techniques like secure multi-party computation (MPC) or differential privacy could be beneficial. Secure Multi-Party Computation (MPC): By integrating MPC protocols into SHATTER, nodes can jointly compute functions over their private inputs without revealing them individually. This would ensure that no single node has access to the complete model or sensitive information. MPC allows for secure computations while maintaining data privacy, making it a valuable addition to SHATTER's privacy defenses. Differential Privacy: Including differential privacy mechanisms in SHATTER can add an extra layer of protection against privacy breaches. By adding noise to the model updates before sharing them, differential privacy ensures that individual data points cannot be distinguished in the aggregated model. This helps in preventing adversaries from extracting sensitive information from the shared model updates. By combining these techniques with SHATTER's existing privacy-preserving mechanisms, the overall privacy guarantees can be strengthened, making it more resilient to various privacy attacks.

What are the potential trade-offs between the privacy benefits and the computational/communication overhead introduced by SHATTER, and how can these be optimized

There are potential trade-offs between the privacy benefits and the computational/communication overhead introduced by SHATTER. These trade-offs can be optimized by carefully balancing privacy requirements with efficiency considerations: Privacy Benefits: SHATTER offers enhanced privacy by sharing model updates in the form of chunks through virtual nodes, reducing the risk of privacy breaches. This privacy-centric approach ensures that sensitive information is not exposed during the collaborative learning process. Computational Overhead: The computational overhead in SHATTER arises from the need to manage multiple virtual nodes and perform additional operations for chunking and aggregation. Optimizing the algorithms and data structures used in SHATTER can help reduce this computational burden without compromising privacy. Communication Overhead: SHATTER increases communication volume due to the exchange of model chunks between virtual nodes. By optimizing the communication protocols, leveraging efficient network topologies, and minimizing redundant data transmission, the communication overhead can be minimized. To optimize these trade-offs, a thorough analysis of the specific requirements of the application, the desired level of privacy, and the available computational resources is essential. Fine-tuning the parameters, optimizing algorithms, and leveraging efficient technologies can help strike a balance between privacy benefits and computational/communication overhead in SHATTER.

Could the SHATTER approach be extended to other collaborative machine learning settings beyond decentralized learning, such as federated learning or cross-silo federated learning

The SHATTER approach can be extended to other collaborative machine learning settings beyond decentralized learning, such as federated learning or cross-silo federated learning, with certain adaptations and considerations: Federated Learning: In federated learning, where a central server coordinates the model training across multiple devices, SHATTER's concept of virtual nodes and model chunking can be applied. Nodes can exchange model updates through virtual nodes to enhance privacy and prevent information leakage. However, the communication topology and protocols may need to be adjusted to suit the federated learning setting. Cross-Silo Federated Learning: In cross-silo federated learning, where data is distributed across different organizations or silos, SHATTER can be customized to ensure secure and private collaboration. By incorporating secure multi-party computation and differential privacy techniques, SHATTER can facilitate collaborative learning while preserving data privacy across silos. Adapting SHATTER to these settings would require addressing specific challenges related to data distribution, network architecture, and security protocols. By tailoring the SHATTER approach to suit the requirements of federated learning and cross-silo federated learning, organizations can leverage its privacy-preserving capabilities in a collaborative machine learning context.