Secure and Byzantine-Robust Decentralized Learning Protocol

Основні поняття

SecureDL is a novel decentralized learning protocol that enhances security and privacy against Byzantine threats through secure multiparty computation and robust aggregation techniques.

Анотація

The content discusses the development of SecureDL, a novel decentralized learning (DL) protocol that aims to enhance security and privacy against Byzantine threats.

Key highlights:

• DL eliminates the need for a central server, making the system more susceptible to privacy attacks and Byzantine threats compared to Federated Learning (FL).
• SecureDL employs secure multiparty computation techniques to enable privacy-preserving aggregation of model updates, preventing clients from accessing other clients' data in plain form.
• The protocol utilizes robust aggregation rules based on cosine similarity and normalization to detect and exclude malicious model updates, enhancing the system's resilience against Byzantine attacks.
• Theoretical analysis is provided to demonstrate the convergence and privacy guarantees of SecureDL.
• Empirical evaluation on MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets shows SecureDL's effectiveness against various Byzantine attacks, even in the presence of a malicious majority.
• The overhead analysis quantifies the computational and communication costs of the privacy-preserving mechanisms in SecureDL.

Статистика

"Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting."
"Defenses against Byzantine adversaries, however, typically require access to the updates of other clients, a counterproductive privacy trade-off that in turn increases the risk of inference attacks on those same model updates."
"Our experiments show that SecureDL is effective even in the case of attacks by the malicious majority (e.g., 80% Byzantine clients) while preserving high training accuracy."

Цитати

"SecureDL facilitates a collaborative defense, while protecting the privacy of clients' model updates through secure multiparty computation."
"By using MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets, we evaluated SecureDL against various Byzantine attacks and compared its effectiveness with four existing defense mechanisms."

Ключові висновки, отримані з

Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness

by Ali Reza Gha... о arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17970.pdf

Глибші Запити

How can SecureDL's aggregation rule be extended to handle more sophisticated Byzantine attacks beyond the ones considered in this work

To extend SecureDL's aggregation rule to handle more sophisticated Byzantine attacks, we can incorporate advanced anomaly detection techniques and outlier rejection mechanisms. One approach could involve leveraging machine learning algorithms to detect patterns of malicious behavior in the model updates. By training a model on historical data of Byzantine attacks, SecureDL can learn to identify subtle deviations or complex manipulations in the updates. Additionally, introducing a consensus mechanism among the clients to verify the legitimacy of updates before aggregation can enhance the protocol's resilience. This consensus mechanism could involve a voting system where clients collectively decide on the validity of each update based on predefined criteria. By combining machine learning-based anomaly detection with client consensus, SecureDL can effectively combat a wider range of Byzantine attacks, including sophisticated and coordinated strategies.

What are the potential limitations of the secure multiparty computation techniques used in SecureDL, and how can they be addressed to further improve the protocol's efficiency and scalability

The secure multiparty computation techniques used in SecureDL may have limitations in terms of computational overhead and communication complexity, which can impact the protocol's efficiency and scalability. One potential limitation is the high computational cost associated with secure comparison, inversion, and square root operations, especially when dealing with large-scale datasets and a large number of clients. To address this, optimizations such as precomputation, parallelization, and batching can be implemented to reduce the computational burden and improve performance. Additionally, exploring more efficient cryptographic primitives and protocols tailored to specific operations within SecureDL can help streamline the secure computation process. Moreover, optimizing the communication protocols for data exchange between clients and minimizing the number of rounds of communication can further enhance the protocol's efficiency and scalability.

Given the decentralized nature of SecureDL, how can the protocol be adapted to handle dynamic client participation and network topology changes during the training process

To adapt SecureDL to handle dynamic client participation and network topology changes during the training process, the protocol can incorporate dynamic reconfiguration mechanisms and adaptive communication strategies. One approach is to implement a dynamic client registration and removal system, where clients can join or leave the network seamlessly without disrupting the training process. This system can involve a registration protocol that allows new clients to securely join the network and contribute to the training while ensuring data privacy and integrity. Additionally, incorporating a dynamic communication protocol that adjusts to changes in network topology, such as node failures or additions, can optimize the message passing and aggregation process. By dynamically adapting to client participation and network changes, SecureDL can maintain robustness and efficiency in decentralized learning environments.