Papers with communication efficiency
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients (2025.acl-long)
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| Challenge: | Existing methods for federated fine-tuning for Large Language Models suffer from performance degradation at low ranks in heterogeneous data settings. |
| Approach: | They propose a low-rank adaptive model with Alternating freeze and Adaptive rank selection which reduces the number of uploaded parameters by 99.8% . |
| Outcome: | The proposed low-rank Adaptation maintains robustness even under extreme heterogeneity and low rank conditions while preserving communication efficiency. |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)
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| Challenge: | Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage. |
| Approach: | They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE. |
| Outcome: | The proposed framework can be used to infer whether a specific relation exists in a private client. |
InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care (2025.emnlp-demos)
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| Challenge: | Existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triages rates. |
| Approach: | They propose to use an AI-driven multilingual TT system to provide decision support for triage. |
| Outcome: | The proposed system achieves word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction. |
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)
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| Challenge: | Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources. |
| Approach: | They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients. |
| Outcome: | The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines. |
OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration (2025.findings-emnlp)
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| Challenge: | Prior work has advanced agent selection and result aggregation, efficient linguistic interactions for deep collaboration among expert agents remain a critical bottleneck. |
| Approach: | They propose a knowledge-aware adaptive collaboration framework to enhance cognitive synergy in multi-agent systems with large language models. |
| Outcome: | The proposed framework improves synergy between agents and language models by enabling agents to dynamically perceive their collaborators’ cognitive states. |
FedSpaLLM: Federated Pruning of Large Language Models (2025.naacl-long)
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| Challenge: | Existing pruning methods assume public access to calibration data, which is impractical for privacy-sensitive applications. |
| Approach: | They propose a federated learning framework for pruning LLMs that prunes models locally based on private data while accounting for system heterogeneity and communication efficiency. |
| Outcome: | The proposed framework reduces communication overhead and personalizes pruning process based on client resources in federated settings. |
EmailSum: Abstractive Email Thread Summarization (2021.acl-long)
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| Challenge: | Recent years have brought about interest in the task of summarizing conversation threads. |
| Approach: | They develop an email thread summarization dataset that contains human-annotated short and long email threads over a wide variety of topics. |
| Outcome: | The proposed dataset contains human-annotated short (30 words) and long (100 words) summaries of 2,549 email threads over a wide variety of topics. |
Spontaneous gestures encoded by hand positions improve language models: An Information-Theoretic motivated study (2023.findings-acl)
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| Challenge: | a key missing step is to explore whether the nonverbal information can be quantified. |
| Approach: | They explore whether incorporating gesture representations can improve the language model’s performance . they also examine whether spontaneous gestures demonstrate entropy rate constancy (ERC) . |
| Outcome: | The proposed model improves the performance of the mixed-modal language models against monologue video data. |
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have emerged as powerful tools for a wide range of tasks, from * Equal Contribution. |
| Approach: | They propose a framework that enhances communication efficiency and task effectiveness in LLM-based multi-agent systems through training. |
| Outcome: | The proposed framework improves communication efficiency and task effectiveness on multi-agent tasks with 2.8x performance gain with less than 10% tokens on tasks requiring heavy information exchange. |
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models (2024.emnlp-main)
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| Challenge: | federated fine-tuning of ODFMs is limited due to their limited size and system heterogeneity . emerging foundation models (FMs) have remarkable zero/few shot learning capabilities . |
| Approach: | They propose a federated fine-tuning method that leverages system and data heterogeneity at the edge. |
| Outcome: | a proposed method for federated fine-tuning improves performance on ODFMs . it allows heterogeneous LoRA ranks across clients for their individual system resources . |
GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients (2024.emnlp-main)
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| Challenge: | Existing projection-based methods that project gradients into a lower-dimensional subspace can introduce computational and memory overheads. |
| Approach: | They propose a novel approach that leverages sparse projections to transform gradients into structured sparser updates. |
| Outcome: | The proposed approach significantly reduces memory usage for optimizer states and minimizes memory footprint, computation, and communication costs, leading to substantial throughput improvements. |
Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)
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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
| Challenge: | Large language models are reshaping internet services, and serving them is costly. |
| Approach: | They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks . |
| Outcome: | The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system. |
LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. |
| Approach: | They propose a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation that uses a training-free equilibrium approximation algorithm to model dialogue over communicative intents and strategies. |
| Outcome: | The proposed framework improves agents’ communication efficiency by helping them convey their intended meaning more effectively through language. |
AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration (2025.acl-long)
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| Challenge: | Existing methods for MAS suffer from high token consumption and inefficiency due to frequent generation and communication among multiple agents. |
| Approach: | They propose a multi-agent system based on large language models that identifies redundant agents and communication across different communication rounds by optimizing the adjacency matrices of the communication graphs and eliminates them to enhance both token efficiency and task performance. |
| Outcome: | The proposed method reduces prompt token consumption and completion token consumption by 18.4% and improves task performance by 1.14. |