Weiming Zhuang
Weiming Zhuang
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Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis
We construct a new benchmark to investigate the performance of federated person re-identification (FedReID), which contains nine datasets with different volumes sourced from different domains to simulate the heterogeneous situation in reality. The benchmark analysis reveals the bottlenecks of FedReID under the real-world scenario, including poor performance of large datasets caused by unbalanced weights in model aggregation and challenges in convergence. To address these issues, we propose three optimization methods: 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.
Weiming Zhuang
,
Xin Gan
,
Yonggang Wen
,
Shuai Zhang
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EasyFL: A Low-code Federated Learning Platform For Dummies
We propose the first low-code FL platform, EasyFL, to enable users with various levels of expertise to experiment and prototype FL applications with little coding. We achieve this goal while ensuring great flexibility and extensibility for customization by unifying simple API design, modular design, and granular training flow abstraction. Besides, EasyFL expedites distributed training by 1.5x.
Weiming Zhuang
,
Xin Gan
,
Yonggang Wen
,
Shuai Zhang
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