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Found 6 papers, 4 papers with code
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.
Federated Unsupervised Domain Adaptation for Face Recognition
To address this problem, we propose federated unsupervised domain adaptation for face recognition, FedFR.
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.
