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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
Exploring Micro Frontends: A Case Study Application in E-...
2025-06-26 · via cs.DC updates on arXiv.org

In the micro frontends architectural style, the frontend is divided into smaller components, which can range from a simple button to an entire page. The goal is to improve scalability, resilience, and team independence, albeit at the cost of increased complexity and infrastructure demands. This paper seeks to understand when it is worth adopting micro frontends, particularly in the context of industry. To achieve this, we conducted an investigation into the state of the art of micro frontends, based on both academic and gray literature. We then implemented this architectural style in a marketplace for handcrafted products, which already used microservices. Finally, we evaluated the implementation through a semi-open questionnaire with the developers. At the studied marketplace company, the need for architectural change arose due to the tight coupling between their main system (a Java monolith) and a dedicated frontend system. Additionally, there were deprecated technologies and poor developer experience. To address these issues, the micro frontends architecture was adopted, along with the API Gateway and Backend for Frontend patterns, and technologies such as Svelte and Fastify. Although the adoption of Micro Frontends was successful, it was not strictly necessary to meet the company's needs. According to the analysis of the mixed questionnaire responses, other alternatives, such as a monolithic frontend, could have achieved comparable results. What made adopting micro frontends the most convenient choice in the company's context was the monolith strangulation and microservices adoption, which facilitated implementation through infrastructure reuse and knowledge sharing between teams.