Deep recommender systems typically involve numerous feature fields for users and items, with a large number of low-frequency features. These low-frequency features would reduce the prediction accuracy with large storage space due to their vast quantity and inadequate training. Some pioneering studies have explored embedding compression techniques to address this issue of the trade-off between storage space and model predictability. However, these methods have difficulty compacting the embedding of low-frequency features in various feature fields due to the high demand for human experience and computing resources during hyper-parameter searching. In this paper, we propose the AutoDPQ framework, which automatically compacts low-frequency feature embeddings for each feature field to an adaptive magnitude. Experimental results indicate that AutoDPQ can significantly reduce the parameter space while improving recommendation accuracy. Moreover, AutoDPQ is compatible with various deep CTR models by improving their performance significantly with high efficiency.

Increasingly stringent data privacy regulations limit the development of person re-identification (ReID) because person ReID training requires centralizing an enormous amount of data that contains sensitive personal information. To address this problem, we introduce federated person re-identification ( FedReID )—implementing federated learning, an emerging distributed training method, to person ReID. FedReID preserves data privacy by aggregating model updates, instead of raw data, from clients to a central server. Furthermore, we optimize the performance of FedReID under statistical heterogeneity via benchmark analysis. We first construct a benchmark with an enhanced algorithm, two architectures, and nine person ReID datasets with large variances to simulate the real-world statistical heterogeneity. The benchmark results present insights and bottlenecks of FedReID under statistical heterogeneity, including challenges in convergence and poor performance on datasets with large volumes. 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, and (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. Extensive experiments demonstrate that these approaches achieve satisfying convergence with much better performance on all datasets. We believe that FedReID will shed light on implementing and optimizing federated learning on more computer vision applications.

Given labeled data in a source domain, unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, whose data distributions are different. However, existing works are inapplicable to face recognition under privacy constraints because they re-quire sharing of sensitive face images between domains. To address this problem, we propose federated unsupervised do-main adaptation for face recognition, FedFR. FedFR jointly optimizes clustering-based domain adaptation and federated learning to elevate performance on the target domain. Specif-ically, 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 do-main training in federated learning. Extensive experiments on a newly constructed benchmark demonstrate that FedFR outperforms the baseline and classic methods on the target domain by 3% to 14% on different evaluation metrics.

Academia and industry have developed several platforms to support the popular privacy-preserving distributed learning method—federated learning (FL). 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. In this article, 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. With only a few lines of code (LOC), EasyFL empowers them with many out-of-the-box functionalities to accelerate experimentation and deployment. These practical functionalities are heterogeneity simulation, comprehensive tracking, distributed training optimization, and seamless deployment. They are proposed based on challenges identified in the proposed FL life cycle. Compared with other platforms, EasyFL not only requires just three LOC (at least$10\times $lesser) to build a vanilla FL application but also incurs lower training overhead. Besides, our evaluations demonstrate that EasyFL expedites distributed training by$1.5\times $. It also improves the efficiency of deployment. We believe that EasyFL will increase the productivity of researchers and democratize FL to wider audiences.

Unsupervised representation learning has achieved outstanding performances using centralized data available on the Internet. However, the increasing awareness of privacy protection limits sharing of decentralized unlabeled image data that grows explosively in multiple parties (e.g., mobile phones and cameras). As such, a natural problem is how to leverage these data to learn visual representations for downstream tasks while preserving data privacy. To address this problem, we propose a novel federated unsupervised learning framework, FedU. In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network. Then, a central server aggregates trained models and updates clients’ models with the aggregated model. It preserves data privacy as each party only has access to its raw data. Decentralized data among multiple parties are normally non-independent and identically distributed (non-IID), leading to performance degradation. To tackle this challenge, we propose two simple but effective methods: 1) We design the communication protocol to upload only the encoders of online networks for server aggregation and update them with the aggregated encoder; 2) We introduce a new module to dynamically decide how to update predictors based on the divergence caused by non-IID. The predictor is the other component of the online network. Extensive experiments and ablations demonstrate the effectiveness and significance of FedU. It outperforms training with only one party by over 5% and other methods by over 14% in linear and semi-supervised evaluation on non-IID data.

Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. It can alleviate privacy concerns of personal re-identification, an important computer vision task. In this work, we implement federated learning to person re-identification (FedReID) and optimize its performance affected by statistical heterogeneity in the real-world scenario. We first construct a new benchmark to investigate the performance of FedReID. This benchmark consists of (1) nine datasets with different volumes sourced from different domains to simulate the heterogeneous situation in reality, (2) two federated scenarios, and (3) an enhanced federated algorithm for FedReID. The benchmark analysis shows that the client-edge-cloud architecture, represented by the federated-by-dataset scenario, has better performance than client-server architecture in FedReID. It also 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. 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. Experiment results demonstrate that our strategies can achieve much better convergence with superior performance on all datasets. We believe that our work will inspire the community to further explore the implementation of federated learning on more computer vision tasks in real-world scenarios.