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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Universal Finite-State and Self-Stabilizing Computation i...
[Submitted on 1 Sep 2024 (v1), last revised 25 Jul 2026 (this ve · 2024-09-01 · via cs.DC updates on arXiv.org

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Abstract:A communication network is said to be "anonymous" if its agents are indistinguishable from each other; it is "dynamic" if its communication links may appear or disappear unpredictably over time. Assuming that each of the $n$ agents of an anonymous dynamic network is initially given an input, it takes $2\tau n$ communication rounds for the agents to compute an arbitrary (frequency-based) function of such inputs (Di Luna-Viglietta, DISC 2023), where $\tau$ is a parameter called "dynamic disconnectivity", and measures how far the network is from being always connected (for always connected dynamic networks, $\tau=1$).
It is known that, without making additional assumptions on the network and without knowing the number of agents $n$, it is impossible to compute most functions and explicitly terminate. In fact, current state-of-the-art algorithms only achieve stabilization, i.e., allow each agent to return an output after every communication round. Outputs can be changed, and are guaranteed to be all correct after $2\tau n$ rounds. Such algorithms rely on the incremental construction of a data structure called "history tree", which is augmented at every round. Thus, they end up consuming an unlimited amount of memory, and are also prone to errors in case of memory loss or corruption.
In this paper, we provide a general self-stabilizing algorithm for anonymous dynamic networks that stabilizes in $\max\{4\tau n-2\mu,2\mu\}$ rounds (where $\mu$ measures the amount of corrupted data initially present in the memory of each agent), as well as a general finite-state algorithm that stabilizes in $\tau(2n^2+n)$ rounds.
Our work improves upon previously known methods that only apply to static networks (Boldi-Vigna, Dist. Comp. 2002). In addition, we develop new fundamental techniques and operations involving history trees, which are of independent interest.

Submission history

From: Giovanni Viglietta [view email]
[v1] Sun, 1 Sep 2024 10:37:52 UTC (142 KB)
[v2] Sun, 27 Oct 2024 10:19:31 UTC (142 KB)
[v3] Sun, 24 Aug 2025 01:54:09 UTC (146 KB)
[v4] Sat, 25 Jul 2026 19:52:54 UTC (147 KB)