Driven by emerging technologies such as edge computing and Internet of Things (IoT), recent years have witnessed the increasing growth of data processing in a distributed way. Federated Learning (FL), a novel decentralized learning paradigm that can unify massive devices to train a global model without compromising privacy, is drawing much attention from both academics and industries. However, the performance dropping of FL running in a heterogeneous and asynchronous environment hinders its wide applications, such as in autonomous driving and assistive healthcare. Motivated by this, we propose a novel mechanism, called Fed2A: Federated learning mechanism in Asynchronous and Adaptive Modes. Fed2A supports FL by (1) allowing clients and the collaborator to work separately and asynchronously, (2) uploading shallow and deep layers of deep neural networks (DNNs) adaptively, and (3) aggregating local parameters by weighing on the freshness of information and representational consistency of model layers jointly. Moreover, the effectiveness and efficiency of Fed2A are also analyzed based on three standard datasets, i.e., FMNIST, CIFAR-10, and GermanTS. Compared with the best performance among three baselines, i.e., FedAvg, FedProx, and FedAsync, Fed2A can reduce the communication cost by over 77%, as well as improve model accuracy and learning speed by over 19% and 76%, respectively.
Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102x faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.
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