Venue: UAI 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area of AI or moderate to high impact on more than one area of AI, with good to excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations. 
review_content: Q1 Summary and contributions: The paper presents a study of the federated clustering of bandits with privacy and communication consideration in addition to the task of regret minimization. A new privacy notion tailored to this setting is also introduced.  An algorithm is proposed for the considered problem and performance guarantees on regret, communication costs and privacy are proven. Experimental results are given to support the theoretical results. 
Q3 Main strengths: 1. The considered problem setting is well-motivated and the introduced ideas seem novel. 

2. The problem is treated extensively and the presented methods appear to be well-suited for the respective problems. 

3. The paper is well-written. The mathematical details are easy to follow. 


Q4 Main weakness: Lack of a lower bound. 
Q5 Detailed comments to the authors: This submission could be improved with the inclusion of a lower bound so it will be clear if the achieved upper bounds are optimal.  

Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q7 Justification for your score: This paper introduces a new problem setting which is well motivated from practical applications. The validity of the provided solutions is validated with theoretical results and experimental results on real data. All things considered, I think this paper will be a good addition to the literature.
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final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept, e.g., novelty, outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: Q1 Summary and contributions: This paper focused on the clustering of bandits problem (CLUB) with the federated learning structure, which highly decreases the communication cost between the servers. Within this setting, the author proposed the Phase-based FCLUB with a CDP algorithm with $O(dL\sqrt{mT})$ regret guarantee.
Q3 Main strengths: This work utilizes the federated learning structure to decrease communication costs and provide a differential privacy guarantee for local information.
Q4 Main weakness: The assumption in section 2 seems too restrictive. It is not reasonable to assume that users with the same preference vector form an underlying cluster, which is somehow impractical from my point of view. A much more reasonable assumption is that the gap between preference vectors within a cluster is small than a threshold.
Q5 Detailed comments to the authors: See Q4.
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q7 Justification for your score: Applying a federated learning structure in the clustering of bandits problem is novel and important. However, the assumption that users with the same preference vector form an underlying cluster is too restrictive to be used in real-world applications.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate to high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations. 
review_content: Q1 Summary and contributions: The paper studies the online clustering of linear bandits, with privacy and communication considerations, by leveraging the federated learning idea. 
Multiple techniques from related areas are carefully combined, such as graph-based clustering, differential privacy, and an asynchronous communication protocol. 
The desired performance is further supported by theoretical results and numerical experiments. 

Q3 Main strengths: 1. The studied problem is meaningful and of potential practical impact. 
2. The technical quality is good, with solid derivations and theoretical results. 
3. The paper is clearly written (though a bit dense in notations). 

Q4 Main weakness: 1. No code is released, which might affect the reproducibility

Q5 Detailed comments to the authors:  
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q7 Justification for your score: Overall, the paper is a nice contribution to the bandit literature. A technically sound method is derived to address a meaningful problem, with rigorous theoretical guarantee and decent experimental supports. As a combination of several existing lines of research and (although non-trivial) of ideas developed therein, I think it is fair to say it has a `moderate` impact and hence I give 6.
review_title: null

Meta-review: Meta Review: This paper studies the federated clustering of bandit (FCLUB) problem, whose task is to minimize the total regret meanwhile satisfying privacy and communication considerations. 
The authors propose a scheme for cluster detection and asynchronous communication protocol for cooperative bandit learning and a modified differential privacy notion on user _cluster_ level.

They provided the theoretical analysis and showed that their algorithm achieved clustered differential privacy, sublinear communication complexity, and sublinear regret. 

The reviewers found that the paper is solid. They gave questions on the lower bound of their regret, the validity of the assumption, and the code availability. The authors' feedback solved their concerns.

The average rating was 6. Two of the three reviewers gave the ratings >=6, and the other 5.
Decision: Accept (Poster)