On the Impact of Malicious and Cooperative Clients on Validation Score-Based Model Aggregation for Federated Learning

Conventional AI-based service flow remains a challenge for IoT-enabled devices since data collected by local clients is transferred to a centralized server, which contains a global machine learning (ML) model. However, this introduces privacy and security concerns for the clients, and federated Learning is positioned to overcome this problem where each client trains a local model with its local data and shares its model parameters with the centralized server instead of sharing data. Upon the receipt of all parameters, it aggregates these parameters and generates a new global model. Later this global model is distributed among the clients. Various aggregation methods have been published for increasing the global model's accuracy performance after aggregation. However, those new aggregation algorithms are not fully investigated under malicious and collaborated environments. A malicious environment is a scenario where malicious clients are present and can share parameters to degrade the aggregated model performance. On the other hand, the collaborative environment is another scenario in which some clients can share information with each other in order to collaborate. To tackle this issue, we investigate a new aggregation method called Score Based Aggregation (SBA) That aims to mitigate the impact of the model parameters from such malicious clients without keeping compromising the training accuracy. We compare our result to a baseline approach where the malicious client is varied from 20% to 50%. Numerical results suggest that the SBA aggregation helps the model maintain the convergence of accuracy at higher levels in comparison to the baseline approach.

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