  Federated Learning (FL) is a decentralized machine learning framework that
enables learning from data distributed across multiple remote devices,
enhancing communication efficiency and data privacy. Due to limited
communication resources, a scheduling policy is often applied to select a
subset of devices for participation in each FL round. The scheduling process
confronts significant challenges due to the need for fair workload
distribution, efficient resource utilization, scalability in environments with
numerous edge devices, and statistically heterogeneous data across devices.
This paper proposes a load metric for scheduling policies based on the Age of
Information and addresses the above challenges by minimizing the load metric
variance across the clients. Furthermore, a decentralized Markov scheduling
policy is presented, that ensures a balanced workload distribution while
eliminating the management overhead irrespective of the network size due to
independent client decision-making. We establish the optimal parameters of the
Markov chain model and validate our approach through simulations. The results
demonstrate that reducing the load metric variance not only promotes fairness
and improves operational efficiency, but also enhances the convergence rate of
the learning models.
