# A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling

## TL;DR

This paper introduces KD-AFRL, a federated reinforcement learning framework with knowledge distillation for multi-domain IoT scheduling, addressing heterogeneity, privacy, and non-IID data challenges, leading to improved efficiency and scalability.

## Contribution

It proposes a novel resource-aware hybrid neural architecture, privacy-preserving federated learning with clustering, and cross-architecture knowledge distillation for heterogeneous IoT environments.

## Key findings

- 21% faster convergence compared to baselines
- 15.7% reduction in completion time
- 3-5 times better performance retention with more domains

## Abstract

The rapid proliferation of Internet of Things (IoT) applications across heterogeneous Cloud-Edge-IoT environments presents significant challenges in distributed scheduling optimization. Existing approaches face issues, including fixed neural network architectures that are incompatible with computational heterogeneity, non-Independent and Identically Distributed (non-IID) data distributions across IoT scheduling domains, and insufficient cross-domain collaboration mechanisms. This paper proposes KD-AFRL, a Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning framework that addresses multi-domain IoT application scheduling through three core innovations. First, we develop a resource-aware hybrid architecture generation mechanism that creates dual-zone neural networks enabling heterogeneous devices to participate in collaborative learning while maintaining optimal resource utilization. Second, we propose a privacy-preserving environment-clustered federated learning approach that utilizes differential privacy and K-means clustering to address non-IID challenges and facilitate effective collaboration among compatible domains. Third, we introduce an environment-oriented cross-architecture knowledge distillation mechanism that enables efficient knowledge transfer between heterogeneous models through temperature-regulated soft targets. Comprehensive experiments with real Cloud-Edge-IoT infrastructure demonstrate KD-AFRL's effectiveness using diverse IoT applications. Results show significant improvements over the best baseline, with 21% faster convergence and 15.7%, 10.8%, and 13.9% performance gains in completion time, energy consumption, and weighted cost, respectively. Scalability experiments reveal that KD-AFRL achieves 3-5 times better performance retention compared to existing solutions as the number of domains increases.