federated learning

We propose a smart multi-tenant FL system, MuFL, to effectively coordinate and execute simultaneous training activities. We first formalize the problem of multi-tenant FL, define multi-tenant FL scenarios, and introduce a vanilla multi-tenant FL system that trains activities sequentially to form baselines. Then, we propose two approaches to optimize multi-tenant FL: 1) activity consolidation merges training activities into one activity with a multi-task architecture; 2) after training it for rounds, activity splitting divides it into groups by employing affinities among activities such that activities within a group have better synergy.

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.

We propose federated unsupervised domain adaptation for face recognition, FedFR. FedFR jointly optimizes clustering-based domain adaptation and federated learning to elevate performance on the target domain. Specifically, for unlabeled data in the target domain, we enhance a clustering algorithm with distance constrain to improve the quality of predicted pseudo labels. Besides, we propose a new domain constraint loss (DCL) to regularize source domain training in federated learning.

We introduce a generalized federated self-supervised learning (FedSSL) framework and conduct in-depth empirical study of FedSSL based on 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. Inspired by the insights, we propose a new approach for model update, FedEMA.

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.

We propose a novel federated unsupervised learning framework, FedU, to learn visual representation from decentralized data while preserving data prviacy. To tackle non-IID challenge, we propose two simple but effective methods: 1) We design the communication protocol to upload and update only the online encoders; 2) We introduce a new module to dynamically decide how to update predictors based on the divergence caused by non-IID.

We present FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID enables in-situ model training on edges with unlabeled data. A cloud server aggregates models from edges instead of centralizing raw data to preserve data privacy. Moreover, to tackle the problem that edges vary in data volumes and distributions, we personalize training in edges with joint optimization of cloud and edge. Specifically, we propose personalized epoch to reassign computation throughout training, personalized clustering to iteratively predict suitable labels for unlabeled data, and personalized update to adapt the server aggregated model to each edge.

We construct a new benchmark to investigate the performance of 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 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.