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Found 6 papers, 3 papers with code
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
Federated learning (FL) enables multiple clients to train a model without compromising sensitive data.
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization
By performing an in-depth analysis of the behavior of a classification model's penultimate layer, we introduce a metric that quantifies the similarity between two clients' data distributions without violating their privacy.
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training
FedDCT reduces the memory requirements and allows low-end devices to participate in FL.
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning
The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning.
An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence and alleviate memory capacity limitations when training large models and/or using high dimension inputs.
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism.
