OpenNovelty.org

Distributed Algorithms for Euclidean Clustering

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
clusteringdistributed algorithmscommunication complexity
Abstract:

We study the problem of constructing (1+ε)(1+\varepsilon)-coresets for Euclidean (k,z)(k,z)-clustering in the distributed setting, where nn data points are partitioned across ss sites. We focus on two prominent communication models: the coordinator model and the blackboard model. In the coordinator model, we design a protocol that achieves a (1+ε)(1+\varepsilon)-strong coreset with total communication complexity O~(sk+dkmin⁡(ε4,ε2+z)+dklog⁡(nΔ))\tilde{O}\left(sk + \frac{dk}{\min(\varepsilon^4,\varepsilon^{2+z})} + dk\log(n\Delta)\right) bits, improving upon prior work (Chen et al., NeurIPS 2016) by eliminating the need to communicate explicit point coordinates in-the-clear across all servers. In the blackboard model, we further reduce the communication complexity to O~(slog⁡(nΔ)+dklog⁡(nΔ)+dkmin⁡(ε4,ε2+z))\tilde{O}\left(s\log(n\Delta) + dk\log(n\Delta) + \frac{dk}{\min(\varepsilon^4,\varepsilon^{2+z})}\right) bits, achieving better bounds than previous approaches while upgrading from constant-factor to (1+ε)(1+\varepsilon)-approximation guarantees. Our techniques combine new strategies for constant-factor approximation with efficient coreset constructions and compact encoding schemes, leading to optimal protocols that match both the communication costs of the best-known offline coreset constructions and existing lower bounds (Chen et al., NeurIPS 2016, Huang et. al., STOC 2024), up to polylogarithmic factors.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper contributes communication-optimal protocols for constructing (1+ε)-coresets in distributed Euclidean (k,z)-clustering, targeting both coordinator and blackboard models. Within the taxonomy, it resides in the Coordinator-Based Communication Models leaf, which contains only three papers total. This leaf sits under Distributed Coreset Construction Protocols, one of four major branches in the field. The small sibling count suggests this is a relatively focused research direction rather than a densely populated area, though the broader Distributed Coreset Construction Protocols branch encompasses multiple communication paradigms.

The taxonomy reveals neighboring leaves addressing Decentralized Communication Models (peer-to-peer and blackboard without central coordination) and MapReduce and Parallel Frameworks (multi-worker paradigms). The paper's dual focus on coordinator and blackboard models bridges these directions: the coordinator protocol aligns with centralized aggregation strategies seen in sibling work, while the blackboard protocol connects to decentralized approaches. The taxonomy's scope notes clarify that coordinator-based methods exclude peer-to-peer architectures, positioning this work at the boundary between centralized and decentralized paradigms within the distributed coreset landscape.

Among thirteen candidates examined, the analysis identifies one refutable pair for the blackboard model contribution, while the coordinator model protocol shows no clear refutation across ten candidates examined. The lazy adaptive sampling and coordinate-wise sampling techniques were not evaluated against prior work in this limited search. The blackboard model contribution, despite one overlapping candidate, still claims improved bounds and upgraded approximation guarantees. The coordinator model contribution appears more distinct within the examined literature, though the search scope remains modest relative to the broader field captured in the thirty-paper taxonomy.

Given the limited search scale—thirteen candidates from semantic matching—the analysis provides a snapshot rather than exhaustive coverage. The paper's position in a small taxonomy leaf with only two siblings suggests it addresses a specific niche within distributed coreset construction. The contribution-level statistics indicate varying degrees of prior work overlap, with the coordinator protocol appearing more novel among examined candidates while the blackboard protocol engages more directly with existing approaches.

Taxonomy

Core-task Taxonomy Papers
30
3
Claimed Contributions
13
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: constructing coresets for Euclidean clustering in distributed settings. The field organizes around four main branches that reflect different emphases in how coresets are built and deployed. Distributed Coreset Construction Protocols focus on communication models and coordination strategies, examining how nodes exchange summaries under various network topologies—ranging from coordinator-based schemes like those in Distributed k-means Topologies[3] and MapReduce k-median[16] to fully decentralized approaches. Federated Learning with Coresets adapts coreset techniques to privacy-sensitive scenarios, as seen in Fedcore[1] and Vertical Federated Coresets[9], where data remains partitioned across clients. Coreset Construction Algorithms and Theory develops the mathematical foundations and sampling strategies that guarantee approximation quality, including work on lightweight constructions like Lightweight Coresets[4] and robust methods such as Robust Coreset Construction[21]. Application-Driven Coreset Systems translate these ideas into domain-specific deployments, from edge computing platforms to vehicular networks, exemplified by Edge Coresets[22] and Peer Vehicle Coresets[19]. Recent lines of work highlight trade-offs between communication overhead, approximation guarantees, and scalability. Many studies explore how hierarchical aggregation or merge-and-reduce paradigms can minimize rounds of communication while preserving clustering quality, whereas others investigate robustness to outliers or fairness constraints. Distributed Euclidean Clustering[0] sits squarely within the Coordinator-Based Communication Models branch, sharing the emphasis on centralized aggregation seen in Distributed PCA k-means[14] and MapReduce k-median[16]. Compared to these neighbors, Distributed Euclidean Clustering[0] appears to refine protocols for merging local coresets under a coordinator, balancing theoretical guarantees with practical efficiency in settings where a central node orchestrates the summarization process.

Claimed Contributions

Communication-optimal protocol for coordinator model

The authors present a distributed clustering protocol for the coordinator model that constructs (1+ε)-coresets using Õ(sk + dk/min(ε⁴,ε²⁺ᶻ) + dk log(n∆)) bits of communication. This improves upon prior work by eliminating the need to communicate explicit point coordinates across all servers and matches known lower bounds up to polylogarithmic factors.

10 retrieved papers
Communication-optimal protocol for blackboard model

The authors develop a distributed clustering protocol for the blackboard model that achieves (1+ε)-approximation guarantees using Õ(s log(n∆) + dk log(n∆) + dk/min(ε⁴,ε²⁺ᶻ)) bits of communication. This improves upon previous constant-factor approximations while reducing communication costs and matching existing lower bounds.

3 retrieved papers
Can Refute
Lazy adaptive sampling and coordinate-wise sampling techniques

The authors introduce lazy adaptive sampling where sites update the blackboard only when local weight estimates change significantly, combined with coordinate-wise sampling that decomposes points along coordinates and samples dimensions based on significance. These techniques enable compact summaries and efficient distributed protocols without transmitting full high-dimensional centers.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Communication-optimal protocol for coordinator model

The authors present a distributed clustering protocol for the coordinator model that constructs (1+ε)-coresets using Õ(sk + dk/min(ε⁴,ε²⁺ᶻ) + dk log(n∆)) bits of communication. This improves upon prior work by eliminating the need to communicate explicit point coordinates across all servers and matches known lower bounds up to polylogarithmic factors.

Contribution

Communication-optimal protocol for blackboard model

The authors develop a distributed clustering protocol for the blackboard model that achieves (1+ε)-approximation guarantees using Õ(s log(n∆) + dk log(n∆) + dk/min(ε⁴,ε²⁺ᶻ)) bits of communication. This improves upon previous constant-factor approximations while reducing communication costs and matching existing lower bounds.

Contribution

Lazy adaptive sampling and coordinate-wise sampling techniques

The authors introduce lazy adaptive sampling where sites update the blackboard only when local weight estimates change significantly, combined with coordinate-wise sampling that decomposes points along coordinates and samples dimensions based on significance. These techniques enable compact summaries and efficient distributed protocols without transmitting full high-dimensional centers.

Table of Contents