Scalable Federated Unlearning via Isolated and Coded Sharding
Abstract
In group decisions or deliberations, stakeholders are often confronted with conflicting opinions. We investigate a logic-based way of expressing such opinions and a formal general notion of a middle ground between stakeholders. Inspired by the literature on preferences with hierarchical and lexicographic models, we instantiate our general framework to the case where stakeholders express their opinions using preference statements of the form ‘I prefer ‘a’ to ‘b’’, where ‘a’ and ‘b’ are alternatives expressed over some attributes, e.g., in a trolley problem, one can express I prefer to save 1 adult and 1 child to 2 adults (and 0 children). We prove theoretical results on the existence and uniqueness of middle grounds. In particular, we show that, for preference statements, middle grounds may not exist and may not be unique. We provide algorithms for deciding the existence and finding middle grounds.
Cite
Text
Lin et al. "Scalable Federated Unlearning via Isolated and Coded Sharding." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/503Markdown
[Lin et al. "Scalable Federated Unlearning via Isolated and Coded Sharding." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/lin2024ijcai-scalable/) doi:10.24963/ijcai.2024/503BibTeX
@inproceedings{lin2024ijcai-scalable,
title = {{Scalable Federated Unlearning via Isolated and Coded Sharding}},
author = {Lin, Yijing and Gao, Zhipeng and Du, Hongyang and Niyato, Dusit and Gui, Gui and Cui, Shuguang and Ren, Jinke},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {4551-4559},
doi = {10.24963/ijcai.2024/503},
url = {https://mlanthology.org/ijcai/2024/lin2024ijcai-scalable/}
}