Resource Allocation for Semantic Communication under Physical-layer Security

Semantic communication is deemed as a revolution of Shannon's paradigm in the six-generation (6G) wireless networks. It aims at transmitting the extracted information rather than the original data, which receivers will try to recover. Intuitively, the larger extracted information, the longer latency of semantic communication will be. Besides, larger extracted information will result in more accurate reconstructed information, thereby causing a higher utility of the semantic communication system. Shorter latency and higher utility are desirable objectives for the system, so there will be a trade-off between utility and latency. This paper proposes a joint optimization algorithm for total latency and utility. Moreover, security is essential for the semantic communication system. We incorporate the secrecy rate, a physical-layer security method, into the optimization problem. The secrecy rate is the communication rate at which no information is disclosed to an eavesdropper. Experimental results demonstrate that the proposed algorithm obtains the best joint optimization performance compared to the baselines.

Optimization in Mobile Augmented Reality Systems for the Metaverse over Wireless Communications

As the essential technical support for Metaverse, Mobile Augmented Reality (MAR) has attracted the attention of many researchers. MAR applications rely on real-time processing of visual and audio data, and thus those heavy workloads can quickly drain the battery of a mobile device. To address such problem, edge-based solutions have appeared for handling some tasks that require more computing power. However, such strategies introduce a new trade-off: reducing the network latency and overall energy consumption requires limiting the size of the data sent to the edge server, which, in turn, results in lower accuracy. In this paper, we design an edge-based MAR system and propose a mathematical model to describe it and analyze the trade-off between latency, accuracy, server resources allocation and energy consumption. Furthermore, an algorithm named LEAO is proposed to solve this problem. We evaluate the performance of the LEAO and other related algorithms across various simulation scenarios. The results demonstrate the superiority of the LEAO algorithm. Finally, our work provides insight into optimization problem in edge-based MAR system for Metaverse.

Oasis: Data Curation and Assessment System for Pretraining of Large Language Models

Data is one of the most critical elements in building a large language model. However, existing systems either fail to customize a corpus curation pipeline or neglect to leverage comprehensive corpus assessment for iterative optimization of the curation. To this end, we present a pretraining corpus curation and assessment platform called Oasis -- a one-stop system for data quality improvement and quantification with user-friendly interactive interfaces. Specifically, the interactive modular rule filter module can devise customized rules according to explicit feedback. The debiased neural filter module builds the quality classification dataset in a negative-centric manner to remove the undesired bias. The adaptive document deduplication module could execute large-scale deduplication with limited memory resources. These three parts constitute the customized data curation module. And in the holistic data assessment module, a corpus can be assessed in local and global views, with three evaluation means including human, GPT-4, and heuristic metrics. We exhibit a complete process to use Oasis for the curation and assessment of pretraining data. In addition, an 800GB bilingual corpus curated by Oasis is publicly released.

ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook

Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers. However, LLMs still fail in many specific tasks although understand the task instruction. In this paper, we focus on the problem of boosting the ability of black-box LLMs to solve downstream tasks. We propose ExpNote, an automated framework to help LLMs better adapt to unfamiliar tasks through reflecting and noting experiences from training data and retrieving them from external memory during testing. We evaluate ExpNote on multiple tasks and the experimental results demonstrate that the proposed method significantly improves the performance of black-box LLMs. The data and code are available at https://github.com/forangel2014/ExpNote

Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions

Food security, a global concern, necessitates precise and diverse data-driven solutions to address its multifaceted challenges. This paper explores the integration of AI foundation models across various food security applications, leveraging distinct data types, to overcome the limitations of current deep and machine learning methods. Specifically, we investigate their utilization in crop type mapping, cropland mapping, field delineation and crop yield prediction. By capitalizing on multispectral imagery, meteorological data, soil properties, historical records, and high-resolution satellite imagery, AI foundation models offer a versatile approach. The study demonstrates that AI foundation models enhance food security initiatives by providing accurate predictions, improving resource allocation, and supporting informed decision-making. These models serve as a transformative force in addressing global food security limitations, marking a significant leap toward a sustainable and secure food future.

User Association and Resource Allocation in Large Language Model Based Mobile Edge Computing System over Wireless Communications

In the rapidly evolving landscape of large language models (LLMs) and mobile edge computing, the need for efficient service delivery to mobile users with constrained computational resources has become paramount. Addressing this, our paper delves into a collaborative framework for model training where user data and model adapters are shared with servers to optimize performance. Within this framework, users initially update the first several layers of the adapters while freezing the other layers of them, leveraging their local datasets. Once this step is complete, these partially trained parameters are transmitted to servers. The servers, equipped with more robust computational capabilities, then update the subsequent layers. After this training, they send the enhanced parameters back to the users. This collaborative training approach ensures that mobile users with limited computational capacities can still benefit from advanced LLM services without being burdened by exhaustive computations. Central to our methodology is the DASHF algorithm, which encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a pioneering fractional programming technique from our recent IEEE JSAC paper "Human-Centric Resource Allocation in the Metaverse over Wireless Communications". The crux of DASHF is its capability to reformulate an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via meticulously crafted transformations, making it solvable by SDR and the Hungarian algorithm. Through extensive simulations, we demonstrate the effectiveness of the DASHF algorithm, offering significant insights for the advancement of collaborative LLM service deployments.

Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach

The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundation models, specifically designed to enhance local task performance on user equipment (UE). Central to our approach is the innovative Emulator-Adapter architecture, segmenting the foundation model into two cohesive modules. This design not only conserves computational resources but also ensures adaptability and fine-tuning efficiency for downstream tasks. Additionally, we introduce an advanced resource allocation mechanism that is fine-tuned to the needs of the Emulator-Adapter structure in decentralized settings. To address the challenges presented by this system, we employ a hybrid multi-agent Deep Reinforcement Learning (DRL) strategy, adept at handling mixed discrete-continuous action spaces, ensuring dynamic and optimal resource allocations. Our comprehensive simulations and validations underscore the practical viability of our approach, demonstrating its robustness, efficiency, and scalability. Collectively, this work offers a fresh perspective on deploying foundation models and balancing computational efficiency with task proficiency.

FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing

The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning these large models, like GPT-3 and BERT, presents challenges, especially in the current foundation model era. We introduce Emulator-Assisted Tuning (EAT) combined with Parameter-Efficient Fine-Tuning (PEFT) to form Parameter-Efficient Emulator-Assisted Tuning (PEAT). Further, we expand this into federated learning as Federated PEAT (FedPEAT). FedPEAT uses adapters, emulators, and PEFT for federated model tuning, enhancing model privacy and memory efficiency. Adapters adjust pre-trained models, while emulators give a compact representation of original models, addressing both privacy and efficiency. Adaptable to various neural networks, our approach also uses deep reinforcement learning for hyper-parameter optimization. We tested FedPEAT in a unique scenario with a server participating in collaborative federated tuning, showcasing its potential in tackling foundation model challenges.

S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models

The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like reasoning and long-context understanding. However, as LLMs are able to process longer contexts, it becomes more challenging to evaluate whether they have acquired certain capabilities, since the length of text (e.g., 100K tokens) they can process far exceeds what humans can reliably assess in a reasonable duration. In this paper, we propose using complex synthetic tasks as a proxy evaluation method, and present S3Eval, a Synthetic, Scalable, Systematic evaluation suite for LLMs evaluation. As a synthetic benchmark, S3Eval enables the creation of any number of evaluation examples that are theoretically invisible to LLMs, mitigating the test set contamination issue. The synthetic nature of S3Eval provides users full control over the dataset, allowing them to systematically probe LLM capabilities by scaling text length and varying task difficulty across diverse scenarios. The strong correlation between S3Eval performance and scores of real-world benchmarks like Big-Bench Hard (BBH) demonstrates the soundness of using S3Eval for evaluation of LLMs. The in-depth analysis also uncover additional insights, including performance drop when the answer is sparsely distributed or located in the middle context, as well as some counter-intuitive trends of model performance.

TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering

Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions. This paper introduces TableQAKit, the first comprehensive toolkit designed specifically for TableQA. The toolkit designs a unified platform that includes plentiful TableQA datasets and integrates popular methods of this task as well as large language models (LLMs). Users can add their datasets and methods according to the friendly interface. Also, pleasantly surprised using the modules in this toolkit achieves new SOTA on some datasets. Finally, \tableqakit{} also provides an LLM-based TableQA Benchmark for evaluating the role of LLMs in TableQA. TableQAKit is open-source with an interactive interface that includes visual operations, and comprehensive data for ease of use.