Search Results for author:
Found 16 papers, 8 papers with code
FedMef: Towards Memory-efficient Federated Dynamic Pruning
To address these challenges, we propose FedMef, a novel and memory-efficient federated dynamic pruning framework.
MAS: Towards Resource-Efficient Federated Multiple-Task Learning
Then, we present our new approach, MAS (Merge and Split), to optimize the performance of training multiple simultaneous FL tasks.
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Theoretically, we show the convergence guarantee of the dual regulators.
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual benefits, presents a unique opportunity to unlock new possibilities in AI research, and address critical challenges in AI and real-world applications.
FedWon: Triumphing Multi-domain Federated Learning Without Normalization
Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients.
TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation
This paper focuses on an under-explored yet important problem: Federated Class-Continual Learning (FCCL), where new classes are dynamically added in federated learning.
Smart Multi-tenant Federated Learning
In this work, we propose a smart multi-tenant FL system, MuFL, to effectively coordinate and execute simultaneous training activities.
Chat-to-Design: AI Assisted Personalized Fashion Design
In this demo, we present Chat-to-Design, a new multimodal interaction system for personalized fashion design.
Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis
Based on these insights, we propose three optimization approaches: (1) We adopt knowledge distillation to facilitate the convergence of FedReID by better transferring knowledge from clients to the server; (2) We introduce client clustering to improve the performance of large datasets by aggregating clients with similar data distributions; (3) We propose cosine distance weight to elevate performance by dynamically updating the weights for aggregation depending on how well models are trained in clients.
Divergence-aware Federated Self-Supervised Learning
Using the framework, our study uncovers unique insights of FedSSL: 1) stop-gradient operation, previously reported to be essential, is not always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL is particularly beneficial for non-IID data.
Federated Unsupervised Domain Adaptation for Face Recognition
To address this problem, we propose federated unsupervised domain adaptation for face recognition, FedFR.
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification
We present FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy.
Collaborative Unsupervised Visual Representation Learning from Decentralized Data
In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network.
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning
To this end, FedFR forms an end-to-end training pipeline: (1) pre-train in the source domain; (2) predict pseudo labels by clustering in the target domain; (3) conduct domain-constrained federated learning across two domains.
EasyFL: A Low-code Federated Learning Platform For Dummies
However, these platforms are complex to use and require a deep understanding of FL, which imposes high barriers to entry for beginners, limits the productivity of researchers, and compromises deployment efficiency.
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
Then we propose two optimization methods: (1) To address the unbalanced weight problem, we propose a new method to dynamically change the weights according to the scale of model changes in clients in each training round; (2) To facilitate convergence, we adopt knowledge distillation to refine the server model with knowledge generated from client models on a public dataset.
