Adaptive Deadline Determination for Mobile Device Selection in Federated Learning
Jaewook Lee, Haneul Ko, Sangheon Pack
Abstract
Owing to dynamically changing resources and channel conditions of mobile devices (MDs), when a static deadline-based MD selection scheme is used for federated learning, resource utilization of MDs can be degraded. To mitigate this problem, we propose an adaptive deadline determination (ADD) algorithm for MD selection, where a deadline for each round is adaptively determined with the consideration of the performance disparity of MDs. Evaluation results demonstrate that ADD can achieve the fastest average convergence time among the comparison schemes.
Topics & Concepts
Computer scienceSelection (genetic algorithm)Convergence (economics)Scheme (mathematics)Mobile deviceChannel (broadcasting)Distributed computingSelection algorithmResource management (computing)Resource (disambiguation)Mobile telephonyComputer networkReal-time computingMobile radioMachine learningOperating systemEconomicsMathematical analysisMathematicsEconomic growthPrivacy-Preserving Technologies in DataAge of Information OptimizationAdvanced MIMO Systems Optimization