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StableFDG: Style and Attention Based Learning for Federated Domain Generalization

Park, Jungwuk; Han, Dong-Jun; Kim, Jinho; Wang, Shiqiang; Brinton, Christopher G; Moon, Jaekyun (November 2023, Conference on Neural Information Processing Systems)

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Federated Split Learning With Joint Personalization-Generalization for Inference- Stage Optimization in Wireless Edge Networks

https://doi.org/10.1109/TMC.2023.3331690

Han, Dong-Jun; Kim, Do-Yeon; Choi, Minseok; Nickel, David; Moon, Jaekyun; Chiang, Mung; Brinton, Christopher G. (November 2023, IEEE Transactions on Mobile Computing)

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SplitGP: Achieving Both Generalization and Personalization in Federated Learning

https://doi.org/10.1109/INFOCOM53939.2023.10229027

Han, Dong-Jun; Kim, Do-Yeon; Choi, Minseok; Brinton, Christopher G.; Moon, Jaekyun (August 2023, IEEE International Conference on Communications (INFOCOM))

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Probabilistic Caching and Dynamic Delivery Policies for Categorized Contents and Consecutive User Demands

https://doi.org/10.1109/TWC.2020.3044076

Choi, Minseok; Molisch, Andreas F.; Han, Dong-Jun; Kim, Dongjae; Kim, Joongheon; Moon, Jaekyun (April 2021, IEEE Transactions on Wireless Communications)
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Cache Allocations for Consecutive Requests of Categorized Contents: Service Provider’s Perspective

https://doi.org/10.1109/WCNC45663.2020.9120507

Choi, Minseok; Molisch, Andreas F.; Han, Dong-Jun; Kim, Joongheon; Moon, Jaekyun (May 2020, 2020 IEEE Wireless Communications and Networking Conference (WCNC))

In wireless caching networks, a user generally has a concrete purpose of consuming contents in a certain preferred category, and requests multiple contents in sequence. While most existing research on wireless caching and delivery has focused only on one-shot requests, the popularity distribution of contents requested consecutively is definitely different from the one-shot request and has been not considered. Also, especially from the perspective of the service provider, it is advantageous for users to consume as many contents as possible. Thus, this paper proposes two cache allocation policies for categorized contents and consecutive user demands, which maximize 1) the cache hit rate and 2) the number of consecutive content consumption, respectively. Numerical results show how categorized contents and consecutive content requests have impacts on the cache allocation.
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