Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model, highlighting the potential risks of DFL communications. In response to these identified risks, this work introduces a security module designed for DFL platforms to counter communication-based attacks. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform called Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario has been deployed, involving eight physical devices implementing three security configurations: (i) a baseline with no security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks. The results indicated an average F1 score of 95 +-3.5 configuration, mitigating the risks posed by eavesdropping or eclipse attacks.


page 1

page 2

page 3

page 4


Security and Privacy Issues of Federated Learning

Federated Learning (FL) has emerged as a promising approach to address d...

Fedstellar: A Platform for Decentralized Federated Learning

In 2016, Google proposed Federated Learning (FL) as a novel paradigm to ...

PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks

The concept of Federated Learning has emerged as a convergence of distri...

Blockchained Federated Learning for Threat Defense

Given the increasing complexity of threats in smart cities, the changing...

Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems

Healthcare IoMT systems are becoming intelligent, miniaturized, and more...

Towards Sybil Resilience in Decentralized Learning

Federated learning is a privacy-enforcing machine learning technology bu...

RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT

Cybercriminals are moving towards zero-day attacks affecting resource-co...

Please sign up or login with your details

Forgot password? Click here to reset