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A federated deep learning framework for privacy preservation and communication efficiency 1 Introduction 1 Related work 2 Distributed deep learning 2 Communication efficiency 2 Privacy-preserving 3 Federated learning 3 Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency (FedPC) 4 Framework overview 4 Master: Analyzing training outcomes from workers and updating global model 4 Workers: Training model and ternarizing evolution of model parameters 5 Convergence and privacy analysis 6 Convergence analysis 6 Privacy analysis 6 Threat model 6 Analysis 6 Experiments 7 Experiment setup 7 Performance analysis 8 Performance with centralized training 8 Performance of FedPC 8 Conclusion 10 Declaration of competing interest 11 Acknowledgment 11 Appendix. Proof of Theorem 1 11 References 12

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Language

UND

Category

nonfiction