Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding

Deep learning-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they are located across different entities. Federated learning (FL) enables multiple clients to collaboratively train a shared model with data privacy guaranteed. However, the domain discrepancy and data scarcity problems among clients deteriorate the performance of the global FL model. To tackle these issues, we propose a novel framework called representation encoding-based federated meta-learning (REFML) for few-shot FD. First, a novel training strategy based on representation encoding and meta-learning is developed. It harnesses the inherent heterogeneity among training clients, effectively transforming it into an advantage for out-of-distribution generalization on unseen working conditions or equipment types. Additionally, an adaptive interpolation method that calculates the optimal combination of local and global models as the initialization of local training is proposed. This helps to further utilize local information to mitigate the negative effects of domain discrepancy. As a result, high diagnostic accuracy can be achieved on unseen working conditions or equipment types with limited training data. Compared with the state-of-the-art methods, such as FedProx, the proposed REFML framework achieves an increase in accuracy by 2.17%-6.50% when tested on unseen working conditions of the same equipment type and 13.44%-18.33% when tested on totally unseen equipment types, respectively

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization

Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. However, FL in wireless networks still faces two major challenges, i.e., large communication overhead and high energy consumption, which are exacerbated by client heterogeneity in dataset sizes and wireless channels. While model quantization is effective for energy reduction, existing works ignore adapting quantization to heterogeneous clients and FL convergence. To address these challenges, this paper develops an energy optimization problem of jointly designing quantization levels, scheduling clients, allocating channels, and controlling computation frequencies (QCCF) in wireless FL. Specifically, we derive an upper bound identifying the influence of client scheduling and quantization errors on FL convergence. Under the longterm convergence constraints and wireless constraints, the problem is established and transformed into an instantaneous problem with Lyapunov optimization. Solving Karush-Kuhn-Tucker conditions, our closed-form solution indicates that the doubly adaptive quantization level rises with the training process and correlates negatively with dataset sizes. Experiment results validate our theoretical results, showing that QCCF consumes less energy with faster convergence compared with state-of-the-art baselines

On Dynamic Resource Allocation for Blockchain Assisted Federated Learning over Wireless Channels

Blockchain assisted federated learning (BFL) has been intensively studied as a promising technology to process data at the network edge in a distributed manner. In this paper, we focus on BFL over wireless environments with varying channels and energy harvesting at clients. We are interested in proposing dynamic resource allocation (i.e., transmit power, computation frequency for model training and block mining for each client) and client scheduling (DRACS) to maximize the long-term time average (LTA) training data size with an LTA energy consumption constraint. Specifically, we first define the Lyapunov drift by converting the LTA energy consumption to a queue stability constraint. Then, we construct a Lyapunov drift-plus-penalty ratio function to decouple the original stochastic problem into multiple deterministic optimizations along the time line. Our construction is capable of dealing with uneven durations of communication rounds. To make the one-shot deterministic optimization problem of combinatorial fractional form tractable, we next convert the fractional problem into a subtractive-form one by Dinkelbach method, which leads to the asymptotically optimal solution in an iterative way. In addition, the closed-form of the optimal resource allocation and client scheduling is obtained in each iteration with a low complexity. Furthermore, we conduct the performance analysis for the proposed algorithm, and discover that the LTA training data size and energy consumption obey an [$\mathcal{O}(1/V)$, $\mathcal{O}(\sqrt{V})$] trade-off. Our experimental results show that the proposed algorithm can provide both higher learning accuracy and faster convergence with limited time and energy consumption based on the MNIST and Fashion-MNIST datasets

Kinetics and specificity of paternal mitochondrial elimination in Caenorhabditis elegans

In most eukaryotes, mitochondria are inherited maternally. The autophagy process is critical for paternal mitochondrial elimination (PME) in Caenorhabditis elegans, but how paternal mitochondria, but not maternal mitochondria, are selectively targeted for degradation is poorly understood. Here we report that mitochondrial dynamics have a profound effect on PME. A defect in fission of paternal mitochondria delays PME, whereas a defect in fusion of paternal mitochondria accelerates PME. Surprisingly, a defect in maternal mitochondrial fusion delays PME, which is reversed by a fission defect in maternal mitochondria or by increasing maternal mitochondrial membrane potential using oligomycin. Electron microscopy and tomography analyses reveal that a proportion of maternal mitochondria are compromised when they fail to fuse normally, leading to their competition for the autophagy machinery with damaged paternal mitochondria and delayed PME. Our study indicates that mitochondrial dynamics play a critical role in regulating both the kinetics and the specificity of PME

Homologs of bacterial heat-labile enterotoxin subunit A contribute to development, stress response, and virulence in filamentous entomopathogenic fungus Beauveria bassiana

IntroductionEnterotoxigenic bacteria commonly excrete heat-labile enterotoxins (LT) as virulence factors that consist of one subunit A (LTA) and five B subunits (LTB). In fungi, there are a large number of genes encoding the homologs of LTA, but their biological roles remain largely unknown. MethodsIn this study, we identified 14 enterotoxin_A domain proteins in filamentous fungus B. bassiana in which five proteins were functionally characterized. ResultsFive proteins displayed diverse sub-cellular localizations but perform convergent functions in stress response, development, and virulence. The loss of five LTA genes resulted in significant reduction in conidial production, blastospore formation, and the increased sensitivity to oxidative and cell wall –perturbing stresses. The virulence of five disruptants was notably weakened as indicated by topical and intrahemocoel injection assays. Notably, the loss of these five proteins led to the significant changes in the carbohydrate profiles of cellular surface, which induced the enhanced host immune reactions of encapsulation and melanization. DiscussionThus, LTA proteins contribute to the fungus–host interaction via maintaining the carbohydrate profiles of cellular surface. This study expands our understanding of the enterotoxin_A domain proteins in fungal physiology and deepens mechanisms involved in the lifestyle of fungal insect pathogens