Venue: NeurIPS 2022 Workshop Federated_Learning

Reviews:

final_score: rating: null
review_content: I am not an expert on the privacy aspect of Federated Learning specifically. However, I think developed can be applied on top of many different Federated Learning algorithms and due to simple analysis, we can derive privacy bounds for those algorithms.

 One question that I have and would be great if authors can clarify is whether can this newly developed mechanism also provide guarantees for pairwise privacy between users.  
review_title: Developed differential privacy could be interesting.

final_score: rating: null
review_content: This paper proposed the I-MVU mechanism that reduces communication cost while providing a differential privacy guarantee. This paper extends the previously introduced MVU mechanism to continuous-valued vectors. This paper could achieve a good tradeoff between utility and privacy. This paper conducted experiments to support their claims. This paper is well written. I don't have any other concerns. 


review_title: review

Decision: Accept (Poster)