Edge-Cloud Hybrid Task Scheduling Using Federated Reinforcement Learning and Adaptive Swarm Intelligence

Abstract This paper presents a novel task scheduling framework for edge cloud environments by integrating Federated Reinforcement Learning (FRL) with an Adaptive Swarm Intelligence based approach. The need for intelligent scheduling algorithms is increasing day by day for executing low latency and energy efficient tasks in distributed loT applications. Preserving data privacy and optimizing performance are two important objectives expected from these techniques. The proposed framework employs FRL to train localized agents at edge nodes for making scheduling decisions while collaboratively improving a global model without raw data exchange. A modified Artificial Bee Colony (ABC) algorithm is integrated to further improve the task allocation process. This algorithm dynamically adapts to resource states and workload characteristics across edge and cloud tiers. The proposed hybrid system jointly optimizes make span, energy consumption and resource utilization. Experimental results on simulated edge cloud workloads demonstrate significant improvements in scheduling efficiency, latency reduction and load balancing compared to existing centralized RL and bio-inspired methods.