Age of information and unbiased federated learning in energy harvesting error-prone channels

Çakır, Zeynep
Federated learning is a communication-efficient and privacy-preserving learning tech nique for collaborative training of machine learning models on vast amounts of data produced and stored locally on the distributed users. In this thesis, unbiased feder ated learning methods that achieve a similar convergence as state-of-the-art federated learning methods in scenarios with various constraints like error-prone channel or in termittent energy availability are investigated. In addition, a prevalent metric called the age of information (AoI), which quantifies the staleness of the information at the destination, is studied under energy constraints and exploited to increase the perfor mance of federated learning algorithms. Firstly, a constrained Markov decision problem that aims to minimize the average age of information over an imperfect channel and under energy constraints is investigated. An optimal threshold-based scheduling policy is obtained and the optimal time aver age AoI and age violation probabilities are derived. Secondly, a federated learning algorithm that jointly designs the unbiased user scheduling and gradient weighting according to the energy and channel profile of each user is presented. It is shown that the proposed algorithm provides a high test accuracy and a convergence guarantees, which is close to the algorithms that have no energy or channel constraints. Lastly, the effect of AoI on federated learning with heterogeneous users and different datasets is studied, and the performance is demonstrated by experiments.


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Citation Formats
Z. Çakır, “Age of information and unbiased federated learning in energy harvesting error-prone channels,” M.S. - Master of Science, Middle East Technical University, 2022.