Backdoor attacks against distributed swarm learning.

Area of Science:

• Machine Learning
• Cybersecurity
• Distributed Systems

Background:

• Traditional machine learning relies on centralized servers, posing security risks from malicious server actors.
• Swarm Learning (SL) is a decentralized paradigm enabling secure, server-less model training by distributing aggregation tasks among participant nodes.

Purpose of the Study:

• To investigate and demonstrate the potential security risks associated with backdoor attacks in Swarm Learning.
• To evaluate the effectiveness of backdoor attacks in different scenarios within the SL framework.
• To explore and propose defense mechanisms against identified backdoor attacks in SL.

Main Methods:

• Simulated backdoor attacks on the Swarm Learning framework.
• Experimentation across various scenarios to assess attack efficacy.
• Development and testing of defense strategies to mitigate identified threats.

Main Results:

• The study successfully demonstrated the feasibility of implanting backdoor attacks in Swarm Learning.
• High attack accuracies were achieved in experiments, confirming the effectiveness of the proposed attack methods.
• The effectiveness of several defense methods in alleviating backdoor attacks was studied.

Conclusions:

• Swarm Learning, despite its decentralized nature, is susceptible to backdoor attacks.
• The findings highlight critical security considerations for the widespread adoption of Swarm Learning.
• Further research into robust defense mechanisms is essential for securing decentralized machine learning environments.