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Found 6 papers, 2 papers with code
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
In this paper, we propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously.
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Prior work has shown that the gradient sharing strategies in federated learning can be vulnerable to data reconstruction attacks.
Neural Tangent Kernel Empowered Federated Learning
The paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the conventional FL paradigms.
Federated Learning via Plurality Vote
Federated learning allows collaborative workers to solve a machine learning problem while preserving data privacy.
Communication-Efficient Federated Learning via Predictive Coding
In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding.
TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation
For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions.
