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cs.DC updates on arXiv.org

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
Design, Implementation, and Analysis of Fair Faucets for ...
Serdar Metin · 2025-06-04 · via cs.DC updates on arXiv.org

The present dissertation addresses the problem of fairly distributing shared resources in non-commercial blockchain networks. Blockchains are distributed systems that order and timestamp records of a given network of users, in a public, cryptographically secure, and consensual way. The records, which may in kind be events, transaction orders, sets of rules for structured transactions etc. are placed within well-defined datastructures called blocks, and they are linked to each other by the virtue of cryptographic pointers, in a total ordering which represents their temporal relations of succession. The ability to operate on the blockchain, and/or to contribute a record to the content of a block are shared resources of the blockchain systems. In commercial networks, these resources are exchanged in return for fiat money, and consequently, fairness is not a relevant problem in terms of computer engineering. In non-commercial networks, however, monetary solutions are not available, by definition. The present non-commercial blockchain networks employ trivial distribution mechanisms called faucets, which offer fixed amounts of free tokens (called cryptocurrencies) specific to the given network. This mechanism, although simple and efficient, is prone to denial of service (DoS) attacks and cannot address the fairness problem. In the present dissertation, the faucet mechanism is adapted for fair distribution, in line with Max-min Fairness scheme. In total, we contributed 6 distinct Max-min Fair algorithms as efficient blockchain faucets. The algorithms we contribute are resistant to DoS attacks, low-cost in terms of blockchain computation economics, and they also allow for different user weighting policies.