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   https://doi.org/10.1111/2041-210X.13790  

   Field, Jasper Toscani; Abrams, A. Jeanine; Cartee, John C.; McTavish, Emily Jane (March 2022, Methods in Ecology and Evolution)
2. Timely Group Updating

   https://doi.org/10.1109/CISS50987.2021.9400248  

   Bastopcu, Melih; Ulukus, Sennur (March 2021, CISS)
3. Queues with Updating Information: Finding the Amplitude of Oscillations

   https://doi.org/10.1142/S0218127422300063  

   Doldo, Philip; Pender, Jamol (March 2022, International Journal of Bifurcation and Chaos)

   Many service systems provide customers with information about the system so that customers can make an informed decision about whether to join or not. Many of these systems provide information in the form of an update. Thus, the information about the system is updated periodically in increments of size [Formula: see text]. It is known that these updates can cause oscillations in the resulting dynamics. However, it is an open problem to explicitly characterize the size of these oscillations when they occur. In this paper, we solve this open problem and show how to exactly calculate the amplitude of these oscillations via a fixed point equation. We also calculate closed form approximations via Taylor expansions of the fixed point equation and show that these approximations are very accurate, especially when [Formula: see text] is large. Our analysis provides new insight for systems that use updates as a way of disseminating information to customers. 

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4. FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

   https://doi.org/10.1609/aaai.v36i7.20785  

   Ruan, Yichen; Joe-Wong, Carlee (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution. We relax this hard association assumption to soft clustered federated learning, which allows every local dataset to follow a mixture of multiple source distributions. We propose FedSoft, which trains both locally personalized models and high-quality cluster models in this setting. FedSoft limits client workload by using proximal updates to require the completion of only one optimization task from a subset of clients in every communication round. We show, analytically and empirically, that FedSoft effectively exploits similarities between the source distributions to learn personalized and cluster models that perform well. 

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5. Deep Partial Updating: Towards Communication Efficient Updating for On-device Inference

   Zhongnan Qu1, Cong Liu2 (January 2022, ECCV)