From: auai.org/UAI/2022/Conference/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: 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.

From: auai.org/UAI/2022/Conference/Paper241/Reviewer_6xCs
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. 
Q2 Assessment of the paper: More detailed information regarding each of these aspects is given below:
Q2(1) Originality/Novelty: 3: Good: The paper makes non-trivial advances over the current state-of-the-art.
Q2(2) Significance/Impact: 3: Good: The paper is likely to have high impact within a subfield of AI OR moderate impact across more than one subfield of AI.
Q2(3) Correctness/Technical quality: 3: Good: The paper appears to be technically sound, but I have not carefully checked the details.
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q2(5) Reproducibility: 3: Good: Key resources (e.g., proofs, code, data) are available and key details (e.g., proofs, experimental setup) are sufficiently well-described for competent researchers to confidently reproduce the main results.
Q2(6) Clarity of writing: 3: Good: The paper is well organized but the presentation could be improved.
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.  

Q6 Overall score: 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. 
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. 
Q8 Confidence in your score: 3: Somewhat confident, but there's a chance I missed some aspects. I did not carefully check some of the details, e.g., novelty, proof of a theorem, experimental design, or statistical validity of conclusions.
Q9 Complying with reviewing instructions: 1: Yes.

From: auai.org/UAI/2022/Conference/Paper241/Reviewer_f5c4
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.
Q2 Assessment of the paper: More detailed information regarding each of these aspects is given below:
Q2(1) Originality/Novelty: 3: Good: The paper makes non-trivial advances over the current state-of-the-art.
Q2(2) Significance/Impact: 3: Good: The paper is likely to have high impact within a subfield of AI OR moderate impact across more than one subfield of AI.
Q2(3) Correctness/Technical quality: 3: Good: The paper appears to be technically sound, but I have not carefully checked the details.
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q2(5) Reproducibility: 3: Good: Key resources (e.g., proofs, code, data) are available and key details (e.g., proofs, experimental setup) are sufficiently well-described for competent researchers to confidently reproduce the main results.
Q2(6) Clarity of writing: 3: Good: The paper is well organized but the presentation could be improved.
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.
Q6 Overall score: 5: Borderline accept: Technically solid paper where reasons to accept, e.g., novelty, outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
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.
Q8 Confidence in your score: 2: Not very confident. I am able to defend my evaluation of some aspects of the paper, but it is quite likely that I missed or did not understand some key details, or can't be sure about the novelty of the work.
Q9 Complying with reviewing instructions: 1: Yes.

From: auai.org/UAI/2022/Conference/Paper241/Reviewer_rPAK
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. 

Q2 Assessment of the paper: More detailed information regarding each of these aspects is given below:
Q2(1) Originality/Novelty: 3: Good: The paper makes non-trivial advances over the current state-of-the-art.
Q2(2) Significance/Impact: 2: Fair: The paper is likely to have moderate impact within a subfield of AI.
Q2(3) Correctness/Technical quality: 3: Good: The paper appears to be technically sound, but I have not carefully checked the details.
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q2(5) Reproducibility: 2: Fair: Key resources (e.g., proofs, code, data) are unavailable but key details (e.g., proof sketches, experimental setup) are sufficiently well-described for an expert to confidently reproduce the main results.
Q2(6) Clarity of writing: 3: Good: The paper is well organized but the presentation could be improved.
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:  
Q6 Overall score: 6: Weak Accept: Technically solid, moderate to high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations. 
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. 	

Q8 Confidence in your score: 3: Somewhat confident, but there's a chance I missed some aspects. I did not carefully check some of the details, e.g., novelty, proof of a theorem, experimental design, or statistical validity of conclusions.
Q9 Complying with reviewing instructions: 1: Yes.
Q10 Ethical concerns (Optional): I do not have any major concerns.