373 research outputs found
SAFA : a semi-asynchronous protocol for fast federated learning with low overhead
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost
FedProf: Selective Federated Learning with Representation Profiling
Federated Learning (FL) has shown great potential as a privacy-preserving
solution to learning from decentralized data that are only accessible to end
devices (i.e., clients). In many scenarios however, a large proportion of the
clients are probably in possession of low-quality data that are biased, noisy
or even irrelevant. As a result, they could significantly slow down the
convergence of the global model we aim to build and also compromise its
quality. In light of this, we propose FedProf, a novel algorithm for optimizing
FL under such circumstances without breaching data privacy. The key of our
approach is a data representation profiling and matching scheme that uses the
global model to dynamically profile data representations and allows for
low-cost, lightweight representation matching. Based on the scheme we
adaptively score each client and adjust its participation probability so as to
mitigate the impact of low-value clients on the training process. We have
conducted extensive experiments on public datasets using various FL settings.
The results show that FedProf effectively reduces the number of communication
rounds and overall time (up to 4.5x speedup) for the global model to converge
and provides accuracy gain.Comment: 23 pages (references and appendices included
Fuzzy-Affine-Model-Based Output Feedback Dynamic Sliding Mode Controller Design of Nonlinear Systems
RISK PRIORITY EVALUATION OF POWER TRANSFORMER PARTS BASED ON HYBRID FMEA FRAMEWORK UNDER HESITANT FUZZY ENVIRONMENT
The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach
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