Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.

Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.

Fog computing is a promising solution to enable delay-sensitive applications in the Internet of Things (IoT). In this paper, based on the simultaneous wireless information and power transfer (SWIPT) technology, we investigate the joint offloading of tasks and energy in fog-enabled IoT networks. Specifically, the task node is allowed to offload energy and tasks to multiple neighboring helper nodes in a time division multiple access (TDMA) manner. When there are no task queues in the nodes, the offloading decision for each task is independent. We first find the offloading strategy to minimize the task execution delay as well as the energy consumption for a specific task and then analyze the condition under which the joint offloading of tasks and energy is beneficial. We show that it becomes more and more desirable to offload both the tasks and the energy from the task node as the number of helper nodes gets large. When there are task queues in the nodes, the offloading decision for each task becomes temporally correlated. We then characterize the optimal strategies to offload the tasks and energy jointly over multiple time slots. An online offloading policy based on the Lyapunov optimization is then proposed to minimize the time average expected delay while stabilizing the system operation. Comprehensive numerical results corroborate our theoretical results and demonstrate the superior performance of the proposed JOTE algorithms.