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   https://doi.org/10.1145/3545947.3576245  

   Lo, Dan; Shahriar, Hossain; Qian, Kai; Whitman, Michael; Wu, Fan; Thomas, Cassandra (March 2022, Proceedings of the 54th ACM Technical Symposium on Computer Science Education (SIGCSE))

   The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills. 

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2. Federated Learning for Healthcare Informatics

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3. Tokenized Incentive for Federated Learning.

   Han, Jingoo; Khan, Ahmad Faraz; Zawad, Syed; Anwar, Ali; Angel, Nathalie Baracaldo; Zhou, Yi; Yan; Feng; Butt, Ali R. (March 2022, International Workshop on Trustable, Verifiable and Auditable Federated Learning (FL-AAAI-22) in Conjunction with AAAI 2022)
4. Controlling Federated Learning for Covertness

   Jain, Adit; Krishnamurthy, Vikram (February 2024, Transactions on machine learning research)

   A learner aims to minimize a function f by repeatedly querying a distributed oracle that provides noisy gradient evaluations. At the same time, the learner seeks to hide arg min f from a malicious eavesdropper that observes the learner’s queries. This paper considers the problem of covert or learner-private optimization, where the learner has to dynamically choose between learning and obfuscation by exploiting the stochasticity. The problem of controlling the stochastic gradient algorithm for covert optimization is modeled as a Markov decision process, and we show that the dynamic programming operator has a supermodular structure implying that the optimal policy has a monotone threshold structure. A computationally efficient policy gradient algorithm is proposed to search for the optimal querying policy without knowledge of the transition probabilities. As a practical application, our methods are demonstrated on a hate speech classification task in a federated setting where an eavesdropper can use the optimal weights to generate toxic content, which is more easily misclassified. Numerical results show that when the learner uses the optimal policy, an eavesdropper can only achieve a validation accuracy of 52% with no information and 69% when it has a public dataset with 10% positive samples compared to 83% when the learner employs a greedy policy. 

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5. Network Update Compression for Federated Learning

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   Kathariya, Birendra; Li, Li; Li, Zhu; Duan, Lingyu; Liu, Shan (January 2020, IEEE Visual Communication & Image Processing (VCIP) Conference)

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