Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries | Paper Citation Record

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This paper cites Communication-efficient learning of deep networks from decentralized data,.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Communication-efficient learning of deep networks from decentralized data,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Federated learning: Challenges, methods, and future directions,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Toward on-device federated learning: A direct acyclic graph-based blockchain approach,

Reference 3

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Fedaux: Leveraging unlabeled auxiliary data in federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Active client selection for clustered federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Federated learning with taskonomy for non-iid data,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Practical and robust federated learning with highly scalable regression training,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Personalized federated graph learning on non-iid electronic health records,

Reference 8

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This paper cites Clustered federated learning in het- erogeneous environment,.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Clustered federated learning in het- erogeneous environment,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Communication-efficient randomized algorithm for multi-kernel online federated learning,

Reference 10

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Tighter regret analysis and optimization of online federated learning,

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This paper cites Federated learning in mobile edge networks: A comprehensive survey,.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Federated learning in mobile edge networks: A comprehensive survey,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Advances and open problems in federated learning,

Reference 13

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries signSGD: Compressed optimisation for non-convex problems,

Reference 14

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns,

Reference 15

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This paper cites signSGD with Majority Vote is Communication Efficient And Fault Tolerant.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries signSGD with Majority Vote is Communication Efficient And Fault Tolerant

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This paper cites Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning

Reference 17

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Sign-based gradient descent with heterogeneous data: Convergence and byzantine resilience,

Reference 18

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This paper cites FedLSC: Improving communi- cation efficiency and robustness in federated learning with stragglers and adversaries,.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries FedLSC: Improving communi- cation efficiency and robustness in federated learning with stragglers and adversaries,

Reference 19

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Exploiting unintended feature leakage in collaborative learning,

Reference 20

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Deep leakage from gradients,

Reference 21

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Inverting gradients-how easy is it to break privacy in federated learning?

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This paper cites Practical secure aggregation for privacy-preserving machine learning,.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Practical secure aggregation for privacy-preserving machine learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Secure single-server aggregation with fault tolerance,

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This paper cites Hi-SAFE: Hierarchical secure aggregation for lightweight federated learning,.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Hi-SAFE: Hierarchical secure aggregation for lightweight federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Scalable and unconditionally secure multiparty computation,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries A hybrid approach to privacy-preserving federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Differentially private secure multi- party computation for federated learning in financial applications,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries DP-SIGNSGD: When Efficiency Meets Privacy and Robustness

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Privacy preserving machine learning with ho- momorphic encryption and federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Privacy-preserving federated learning based on multi-key homomorphic encryption,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Homomorphic encryption for arithmetic of approximate numbers,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Efficient fully homomorphic encryption from (standard) lwe,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Fully homomorphic encryption using ideal lattices,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Efficient multiparty protocols using circuit randomization,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Communication-Efficient (Client-Aided) Secure Two-Party Protocols and Its Application

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries ATLAS: efficient and scalable mpc in the honest majority setting,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Applications of fermat’s little theorem in cryptography,

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This paper cites Communication-efficient learning of deep networks from decentralized 15 data,.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Communication-efficient learning of deep networks from decentralized 15 data,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Goldreich,Foundations of Cryptography: Volume 2–Basic Applica- tions

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Secure multiparty computation for privacy- preserving data mining,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries AHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Correlations of the Riemann zeta function

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries PQSF: Post-quantum secure privacy-preserving federated learning,

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Secure and flexible privacy- preserving federated learning based on multi-key fully homomorphic encryption,

Reference 48

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This paper cites von zur Gathen and J.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries von zur Gathen and J

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Enhanced spin injection efficiency in a four-terminal double quantum dot system

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Gradient-based learning applied to document recognition,

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries Learning multiple layers of features from tiny images,

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