Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
<|reference_start|>Trust Driven On-Demand Scheme for Client Deployment in Federated Learning: Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for each learning iteration. However, doubts arise about the trustworthiness of devices deployed as clients in FL scenarios, especially when container deployment processes are involved. Addressing these challenges is important, particularly in managing potentially malicious clients capable of disrupting the learning process or compromising the entire model. In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture. This is a feature lacking in the initial client selection and deployment mechanism of the On-Demand architecture. We introduce a trust mechanism, named "Trusted-On-Demand-FL", which establishes a relationship of trust between the server and the pool of eligible clients. Utilizing Docker in our deployment strategy enables us to monitor and validate participant actions effectively, ensuring strict adherence to agreed-upon protocols while strengthening defenses against unauthorized data access or tampering. Our simulations rely on a continuous user behavior dataset, deploying an optimization model powered by a genetic algorithm to efficiently select clients for participation. By assigning trust values to individual clients and dynamically adjusting these values, combined with penalizing malicious clients through decreased trust scores, our proposed framework identifies and isolates harmful clients. This approach not only reduces disruptions to regular rounds but also minimizes instances of round dismissal, Consequently enhancing both system stability and security.<|reference_end|>
@article{chahoud2024trust,
    title={Trust Driven On-Demand Scheme for Client Deployment in Federated
  Learning},
    author={Mario Chahoud, Azzam Mourad, Hadi Otrok, Jamal Bentahar, Mohsen
  Guizani},
    journal={arXiv preprint arXiv:2405.00395},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00395},
    primaryClass={cs.CR cs.AI}
}