Search Results for author:
Found 8 papers, 2 papers with code
Convergence Analysis of Split Federated Learning on Heterogeneous Data
We further extend the analysis to non-convex objectives and where some clients may be unavailable during training.
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Toward the challenges, we propose an online problem approximation to reduce the problem complexity and optimize the resources to balance the needs of model training and inference.
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
In this paper, we propose Federated knowledge distillation enabled by Adversarial Learning (FedAL) to address the data heterogeneity among clients.
Incentive Mechanism Design for Distributed Ensemble Learning
To this end, we propose an alternating algorithm that iteratively updates each learner's training data size and reward.
Lightweight Self-Knowledge Distillation with Multi-source Information Fusion
Specifically, we introduce a Distillation with Reverse Guidance (DRG) method that considers different levels of information extracted by the model, including edge, shape, and detail of the input data, to construct a more informative teacher.
Optimization Design for Federated Learning in Heterogeneous 6G Networks
With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge.
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer.
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity.
