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Found 6 papers, 1 papers with code
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems.
Efficient Semi-Supervised Federated Learning for Heterogeneous Participants
Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data.
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data.
Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability
In this work, after theoretically quantifying the estimation error bound and the manipulating risk bound of the Laplace mechanism, we propose two mechanisms improving the usefulness and soundness simultaneously: the weighted sampling mechanism and the additive mechanism.
A vision based system for underwater docking
In the experiments, we first evaluate performance of the proposed detection module on UDID and its deformed variations.
Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
One of the fundamental issue in ensemble learning is the trade-off between classification accuracy and computational costs, which is the goal of ensemble pruning.
