Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.16987

MODAIC: Coordinated Asynchrony for Decentralized Multimodal Learning

Yanhang Shi·Xiaoyu Wang·Houwei Cao·Jian Li·Yong Liu
View original source ↗Read source document ↗Export metadata ↓

Abstract

Multimodal decentralized learning enables collaboration across heterogeneous edge devices without sharing raw data, but differing modality sets create coupled computation and communication bottlenecks. Multimodal agents support cross-modal alignment while training more model components and sharing bandwidth across multiple modality overlays. Under bandwidth limits, these agents can become hot spots: transfers miss aggregation windows, and their updates become stale or underrepresented, degrading worst-agent accuracy. Asynchrony removes global blocking but does not by itself coordinate local cross-modal interaction with timely peer-update exchange. We present MODAIC (Modality-Decoupled Asynchrony with Interleaved Coordination), a wait-free system that coordinates local modality progress, peer-to-peer update delivery, and aggregation under heterogeneous compute and bandwidth. Hierarchical asynchrony lets modalities progress independently while enforcing reciprocal feature exchange; load-aware update fetching overlaps training with communication, limits concurrent serving load, and reuses late arrivals; and consensus-aware peer selection uses lightweight metadata to account for update availability, peer participation, and loss similarity without active probing. We implement MODAIC as a containerized multi-agent framework and evaluate it on local and cross-region cloud testbeds. Across AVE and KU-HAR, MODAIC improves average accuracy and multimodal worst-agent performance. On AVE at 200 Mbps per agent, it improves multimodal worst-agent accuracy by 11.0 percentage points at 1000 seconds over the strongest baseline. On the cloud testbed, it reduces time-to-accuracy by up to 30%. Ablations show that simpler load-balancing and delayed-aggregation variants leave a worst-agent accuracy gap, and stress tests show tolerance to WAN variability and partial tracker unavailability.

Explore related subjects

cs.DCcs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yanhang Shi, Xiaoyu Wang, Houwei Cao, Jian Li, Yong Liu. 2026-10-12. MODAIC: Coordinated Asynchrony for Decentralized Multimodal Learning. https://arxiv.org/abs/2610.16987

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

PerCache: Predictive Hierarchical Cache for RAG Applications on Mobile Devices

Retrieval-augmented generation (RAG) has become a de facto paradigm in various large language model (LLM)-driven applications on mobile devices, such as mobile assistants leveraging personal emails or meeting records. However, due to lengthy prompts and resource constraints, mobile RAG systems exhibit significantly high response latency. To address this issue, one promising approach is to reuse intermediate computational results across different queries to eliminate redundant computation. But most existing approaches, such as key-value (KV) cache reuse and semantic cache reuse, are designed for cloud settings and perform poorly on mobile devices as they overlook the distinctive characteristics of mobile RAG. We propose PerCache, a novel hierarchical cache framework designed for reducing end-to-end latency of personalized RAG applications on mobile platforms. PerCache adopts a hierarchical architecture that progressively matches similar queries and query-key-value (QKV) cache to maximize the reuse of intermediate results at different computing stages. To improve cache hit rate, PerCache applies a predictive method to populate the cache with queries that are likely to be raised in the future. In addition, PerCache can adapt its configurations to dynamic system loads, aiming to maximize caching utility with constrained resource consumption. We implement PerCache on top of an existing mobile LLM inference engine and deploy it on commodity mobile phones. Extensive evaluations show that PerCache achieves the lowest average end-to-end latency across five datasets and two LLM architectures, reducing latency by up to 63.5% over the strongest baseline while maintaining comparable answer quality.

cs.DC↗
arXiv2026

Dynamic Resource Management in Production HPC Clusters

Many large-scale scientific applications exhibit time-varying behavior, yet production HPC clusters still rely on rigid, fixed-size allocations, and most dynamic techniques remain confined to laboratory prototypes. This work presents a practical MPI malleability methodology that integrates with state-of-the-art high-performance computing (HPC) software stacks and operational practices. The methodology is implemented in the Dynamic Management of Resources (DMR) framework and is designed to ease adoption by existing applications without requiring intrusive code changes or scheduler modifications. We evaluate our approach by integrating the DMR API into two large-scale scientific applications and deploying them on three TOP500 supercomputers under realistic production configurations. Our non-invasive malleability solution achieves performance comparable to static baselines in controlled environments while substantially reducing node-hour consumption for identical workloads. These results show that malleability can be effectively exploited on production systems using vanilla resource managers, lowering the barrier to adoption of dynamic resource management in HPC.

cs.DC↗
arXiv2026

Purlin: Separating Orchestration from the Datapath of Collectives

Distributed inference depends on GPU collective communication that must keep pace with evolving hardware and specialized workloads. However, existing works often couple the semantics, orchestration (where and when data moves), and datapath (how data moves) of collective communication. This coupling makes it costly to adopt new hardware mechanisms and customize communication for applications. We present Purlin, a scale-up communication framework that separates these concerns. At the top of Purlin, we specify collectives as a naming of an input and output layout and a copy or reduction operation. In the middle, we introduce a shared orchestration protocol, Stage, Notify, And Consume (SNAC), which derives coordination from these specifications. Below SNAC sits a hardware-specific datapath we call Atom, which implements two key data movement primitives for collectives: copy and reduce. This separation lets us customize collectives and adopt new hardware mechanisms while reusing orchestration via SNAC. We evaluate Purlin on A100, H200, and B200 GPUs. Across seven collectives, Purlin achieves latency speedups of up to 5.14x and bandwidth improvements of up to 4.50x over baselines. Integrated into SGLang, Purlin improves offline LLM serving throughput and interactivity by 1.13x on average and up to 1.37x over baselines. For online LLM inference, Purlin improves interactivity by 1.26x on average and up to 2.85x, with the largest gain occurring under overload. For diffusion image generation, Purlin reduces end-to-end latency by up to 1.13x.

cs.DC↗