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1. Differentially-Private Collaborative Online Personalized Mean Estimation

   https://doi.org/10.1109/ISIT54713.2023.10206796  

   Yakimenka, Yauhen; Weng, Chung-Wei; Lin, Hsuan-Yin; Rosnes, Eirik; Kliewer, Jörg (June 2023, IEEE)
2. Iterative Collaborative Filtering for Sparse Matrix Estimation

   https://doi.org/10.1287/opre.2021.2193  

   Borgs, Christian; Chayes, Jennifer T.; Shah, Devavrat; Yu, Christina Lee (December 2021, Operations Research)

   We consider sparse matrix estimation where the goal is to estimate an n-by-n matrix from noisy observations of a small subset of its entries. We analyze the estimation error of the popularly used collaborative filtering algorithm for the sparse regime. Specifically, we propose a novel iterative variant of the algorithm, adapted to handle the setting of sparse observations. We establish that as long as the number of entries observed at random scale logarithmically larger than linear in n, the estimation error with respect to the entry-wise max norm decays to zero as n goes to infinity, assuming the underlying matrix of interest has constant rank r. Our result is robust to model misspecification in that if the underlying matrix is approximately rank r, then the estimation error decays to the approximation error with respect to the [Formula: see text]-norm. In the process, we establish the algorithm’s ability to handle arbitrary bounded noise in the observations. 

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3. Differentially-Private Collaborative Online Personalized Mean Estimation

   https://doi.org/10.1109/TIFS.2025.3612276  

   Yakimenka, Yauhen; Weng, Chung-Wei; Lin, Hsuan-Yin; Rosnes, Eirik; Kliewer, Jörg (September 2025, IEEE Transactions on Information Forensics and Security)
4. Collaborative Channel Estimation in Backscattering Tag-to-Tag Networks

   https://doi.org/10.1145/3360773.3360882  

   Ahmad, Abeer; Athalye, Akshay; Stanacević, Milutin; Das, Samir R. (January 2019, DFHS'19: Proceedings of the 1st ACM International Workshop on Device-Free Human Sensing)
5. Towards collaborative intelligence: routability estimation based on decentralized private data

   https://doi.org/10.1145/3489517.3530578  

   Pan, Jingyu; Chang, Chen-Chia; Xie, Zhiyao; Li, Ang; Tang, Minxue; Zhang, Tunhou; Hu, Jiang; Chen, Yiran (July 2022, The 59th ACM/IEEE Design Automation Conference)

   Applying machine learning (ML) in design flow is a popular trend in Electronic Design Automation (EDA) with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow. 

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