# Trust Driven On-Demand Scheme for Client Deployment in Federated Learning

arXiv: https://arxiv.org/abs/2405.00395 · HTML: https://mlsanity.com/paper/2405.00395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging](https://mlsanity.com/paper/1909.05125/) (2019)
- [Central Server Free Federated Learning over Single-sided Trust Social Networks](https://mlsanity.com/paper/1910.04956/) (2019)
- [Clustered Vehicular Federated Learning: Process and Optimization](https://mlsanity.com/paper/2201.11271/) (2022)
- [Less is More: A privacy-respecting Android malware classifier using Federated Learning](https://mlsanity.com/paper/2007.08319/) (2020)
- [ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme](https://mlsanity.com/paper/2211.02906/) (2022)
- [Revisiting Agglomerative Clustering](https://mlsanity.com/paper/2005.07995/) (2020)
- [FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices](https://mlsanity.com/paper/2211.01805/) (2022)
- [ModularFed: Leveraging Modularity in Federated Learning Frameworks](https://mlsanity.com/paper/2212.10427/) (2022)

## Built on by (most cited first, 1 of 1)

- [On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning](https://mlsanity.com/paper/2405.07175/) (2024)

## Abstract (arXiv)

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.