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On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning

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

TL;DR

The paper addresses the challenge of limited and shifting data availability in Federated Learning by proposing On-Demand deployment of Docker-contained FL clients guided by a Deep Reinforcement Learning framework. It introduces a novel MDP-based DRL architecture with a Master Learner trained offline and a Joiner Learner refined online to optimize dynamic client provisioning, placement, and data diversity, leveraging Kubeadm orchestration and Docker containers. Key contributions include an end-to-end architecture, a multi-objective cost function balancing resource use, data variety, and orchestrator demand, and a DRL-enabled deployment pipeline that achieves faster convergence and higher client participation in dynamic settings. Experimental results on the MDC dataset show improved data availability, reduced rounds to target accuracy, and resilience to data shifts, highlighting practical implications for scalable, privacy-preserving FL in mobile and fog contexts.

Abstract

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby enhancing adaptability. However, challenges arise in dynamic and mobile environments where certain devices may become inaccessible as FL clients, impacting data availability and client selection methods. To address this, we propose an On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing Deep Reinforcement Learning (DRL), targets client availability and selection, while considering data shifts, and container deployment complexities. It employs an autonomous end-to-end solution for handling model deployment and client selection. The DRL strategy uses a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner. The designed cost functions represent the complexity of the dynamic client deployment and selection. Simulated tests show that our architecture can easily adjust to changes in the environment and respond to On-Demand requests. This underscores its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and tabular reinforcement learning solutions.

On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning

TL;DR

The paper addresses the challenge of limited and shifting data availability in Federated Learning by proposing On-Demand deployment of Docker-contained FL clients guided by a Deep Reinforcement Learning framework. It introduces a novel MDP-based DRL architecture with a Master Learner trained offline and a Joiner Learner refined online to optimize dynamic client provisioning, placement, and data diversity, leveraging Kubeadm orchestration and Docker containers. Key contributions include an end-to-end architecture, a multi-objective cost function balancing resource use, data variety, and orchestrator demand, and a DRL-enabled deployment pipeline that achieves faster convergence and higher client participation in dynamic settings. Experimental results on the MDC dataset show improved data availability, reduced rounds to target accuracy, and resilience to data shifts, highlighting practical implications for scalable, privacy-preserving FL in mobile and fog contexts.

Abstract

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby enhancing adaptability. However, challenges arise in dynamic and mobile environments where certain devices may become inaccessible as FL clients, impacting data availability and client selection methods. To address this, we propose an On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing Deep Reinforcement Learning (DRL), targets client availability and selection, while considering data shifts, and container deployment complexities. It employs an autonomous end-to-end solution for handling model deployment and client selection. The DRL strategy uses a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner. The designed cost functions represent the complexity of the dynamic client deployment and selection. Simulated tests show that our architecture can easily adjust to changes in the environment and respond to On-Demand requests. This underscores its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and tabular reinforcement learning solutions.
Paper Structure (24 sections, 11 equations, 7 figures, 2 algorithms)

This paper contains 24 sections, 11 equations, 7 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Proposed Architecture
  4. Joiner Learning
  5. Master Learner
  6. Kubeadm Environment Initialization
  7. Fog Manager
  8. Client Manager
  9. Data Advisor
  10. Aggregator
  11. Orchastrator Manager
  12. Client Profiler
  13. Log Reporter
  14. Learning model
  15. Log Repository
  16. ...and 9 more sections

Figures (7)

  • Figure 1: 2021 vs. 2022 Volume of Privacy Requests Per Million WinNT
  • Figure 2: Overall Flow of The Proposed Architecture.
  • Figure 3: An instantaneous view of the data volume of records accessible for learning, comparing the default clients with those dynamically deployed On-Demand.
  • Figure 4: Cost Evolution Over Time
  • Figure 5: Accuracy with Relation to the Number of Rounds
  • ...and 2 more figures