  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.
