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

Mario Chahoud, Azzam Mourad, Hadi Otrok, Jamal Bentahar, Mohsen Guizani

TL;DR

Trusted-On-Demand-FL addresses the security and reliability challenges of deploying FL clients on potentially untrusted devices by integrating a dynamic trust mechanism into container-based On-Demand FL. The architecture combines Docker/Kubeadm containerization, two-step verification, context-sharing checks, and a genetic-algorithm-based multi-objective optimization to select and deploy trusted volunteers across regions. Key contributions include a bootstrapped initial-trust generator, a continuous trust update pipeline, and a GA that optimizes deployment with objectives for trust, diversity, and resource utilization, demonstrated on the MDC dataset. The approach enhances robustness to malicious participants, reduces rounds to convergence, and expands the pool of trustworthy clients, offering practical gains for scalable, secure FL in dynamic environments where devices join and leave heterogeneously.

Abstract

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.

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

TL;DR

Trusted-On-Demand-FL addresses the security and reliability challenges of deploying FL clients on potentially untrusted devices by integrating a dynamic trust mechanism into container-based On-Demand FL. The architecture combines Docker/Kubeadm containerization, two-step verification, context-sharing checks, and a genetic-algorithm-based multi-objective optimization to select and deploy trusted volunteers across regions. Key contributions include a bootstrapped initial-trust generator, a continuous trust update pipeline, and a GA that optimizes deployment with objectives for trust, diversity, and resource utilization, demonstrated on the MDC dataset. The approach enhances robustness to malicious participants, reduces rounds to convergence, and expands the pool of trustworthy clients, offering practical gains for scalable, secure FL in dynamic environments where devices join and leave heterogeneously.

Abstract

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.
Paper Structure (26 sections, 13 equations, 6 figures, 3 algorithms)

This paper contains 26 sections, 13 equations, 6 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Literature review
  3. Proposed Architecture
  4. Architecture Overview
  5. Communication Manager
  6. Aggregator
  7. Kubeadm Containerization Required Modules
  8. Kubeadm Environment Initializer
  9. Client Manager
  10. Fog Client
  11. Initial Trust Generator
  12. Learning Respond
  13. Context Sharing
  14. Trust Detector
  15. Module for Decision-Making in the Selection and Deployment of Trusted Volunteer Clients
  16. ...and 11 more sections

Figures (6)

  • Figure 1: Overall Architecture.
  • Figure 2: A snapshot on the number of available trusted devices in different area locations at a time t.
  • Figure 3: Accuracy progress of the centralized model, static devices architecture, and our proposed solution in relation to the progression of learning rounds.
  • Figure 4: The Accuracy Levels of Trusted-On-Demand-Fl while using random trust initialization vs bootstrapping.
  • Figure 5: This graph shows the trend of certain devices deviating from their typical clusters while assessing the performance of our solution with and without these devices.
  • ...and 1 more figures