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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
ConflictSync: Bandwidth Efficient Synchronization of Dive...
Pedro Silva Gomes, Miguel Boaventura Rodrigues, Carlos Baquero · 2025-05-02 · via cs.DS updates on arXiv.org

State-based Conflict-free Replicated Data Types (CRDTs) are widely used in distributed systems to ensure high availability without coordination. However, their naive synchronization strategy - transmitting the full state - incurs high communication costs. Existing optimizations like delta-CRDTs reduce this overhead but rely on external metadata that must be garbage collected to prevent unbounded growth, at the cost of full state transmissions after network partitions. This paper presents ConflictSync, the first digest-driven synchronization algorithm for state-based CRDTs. We reduce synchronization to the set reconciliation of irredundant join decompositions and build on existing work in rateless set reconciliation. To support CRDTs, we generalize set reconciliation to variable-sized elements, and further introduce a novel combination of Bloom filters with Rateless Invertible Bloom Lookup Tables to address inefficiencies at low similarity levels. Our evaluation shows that ConflictSync reduces total data transfer by up to 18 times compared to traditional state-based synchronization. Bloom filter prefiltering reduces overhead by up to 50% compared to pure rateless reconciliation at 0% similarity, while pure rateless reconciliation performs better above 93% similarity. We characterize the trade-off between similarity level and Bloom filter size, identifying optimal configurations for different synchronization scenarios. Although developed for CRDTs, ConflictSync applies to any synchronization problem where states can be decomposed into sets of constituent components, analogous to join decompositions, making it suitable for a wide range of distributed data models.