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저자 홍만수, 강석규, 이지형 
논문지 Applied Sciences 
Vol. 12 
No. 5806 
pp.  
게재일 2022-06-07 
Federated learning, a data privacy-focused distributed learning method, trains a model by aggregating local knowledge from clients. Each client collects and utilizes its own local dataset to train a local model. Local models in the connected federated learning network are uploaded to the server. In the server, local models are aggregated into a global model. During the process, no local data is transmitted in or out of any client. This procedure may protect data privacy; however, federated learning has a worse case of example forgetting problem than centralized learning. The problem manifests in lower performance in testing. We propose federated weighted averaging (FedWAvg). FedWAvg identifies forgettable examples in each client and utilizes that information to rebalance local models via weighting. By weighting clients with more forgettable examples, such clients are better represented and global models can acquire more knowledge from normally neglected clients. FedWAvg diminishes the example forgetting problem and achieve better performance. Our experiments on SVHN and CIFAR-10 datasets demonstrate that our proposed method gets improved performance compared to existing federated learning algorithm in non-IID settings, and that our proposed method can palliate the example forgetting problem. 

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      Adversarial Detection with Gaussian Process Regression-based Detector
      2019.09.02
      저자: Sangheon Lee, Noo-ri Kim, Youngwha Cho, Jae-Young Choi, Suntae Kim, Jeong-Ah Kim, Jee-Hyong Lee     논문지: KSII Transactions on Internet and Information Systems     Vol.: 13     No.: 8     pp.: 4285-4299     게재일: 2019-08-01    

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