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Found 5 papers, 2 papers with code
How to Forget Clients in Federated Online Learning to Rank?
In a FOLTR system, a ranker is learned by aggregating local updates to the global ranking model.
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems
For this, FOLTR trains learning to rank models in an online manner -- i. e. by exploiting users' interactions with the search systems (queries, clicks), rather than labels -- and federatively -- i. e. by not aggregating interaction data in a central server for training purposes, but by training instances of a model on each user device on their own private data, and then sharing the model updates, not the data, across a set of users that have formed the federation.
Is Non-IID Data a Threat in Federated Online Learning to Rank?
A well-known factor that affects the performance of federated learning systems, and that poses serious challenges to these approaches, is that there may be some type of bias in the way data is distributed across clients.
Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning
Paraphrase generation is a fundamental and long-standing task in natural language processing.
