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

Time Minimization in Hierarchical Federated Learning

Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts. To solve the problem, an iterative algorithm is proposed. After that, a time-minimized UE-to-edge association algorithm is presented where the maximum latency of the system is reduced. Simulation results show that the global model converges faster under optimal edge server and local iteration counts. The hierarchical federated learning latency is minimized with the proposed UE-to-edge association strategy.Comment: This paper appears in the Proceedings of 2022 ACM/IEEE Symposium on Edge Computing (SEC). Please feel free to contact us for questions or remark

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

Analysis of factors influencing variation in soil respiration between forest types : a case study at Bukit Timah Nature Reserve

Tropical forests have been facing rapid degradation in recent decades, which alters pristine primary forest into secondary forests. Despite there being increasing portions of secondary forests, few studies are explaining how rapid degradation of pristine forests influences carbon balance and dynamics in a tropical forest. Here we studied the spatial patterns of soil respiration, which is a crucial process influencing terrestrial carbon balance. Our experiment was conducted at 100 measurement locations within a primary and secondary tropical rainforest in Bukit Timah Nature Reserve, Singapore. We aimed to investigate whether soil respiration rates differed between both forest types, and what the underlying abiotic and biotic factors influencing the differences were. We hypothesize that the soil respiration rates in the secondary forest would be higher than that of the primary forest, and that the factors we analyzed positively influence soil respiration rates. Results showed that the soil respiration rates observed in the secondary forest was higher than that of the primary forest. However, none of the tested factors (soil temperature, soil moisture, diameter of trees at breast height, canopy cover) were significantly influencing soil respiration rates. Our findings suggest that the factors tested in our study were not crucial in governing spatial pattern of soil respiration at our site and that other factors such as soil organic carbon content and species composition are more important factors influencing spatial variation of soil respiration between primary and secondary tropical forests.Bachelor of Science in Environmental Earth Systems Scienc

Virtual reality in metaverse over wireless networks with user-centered deep reinforcement learning

The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the possibility for highly immersive and interactive socialization. Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, and especially so in the context of socialization. As the virtual world 3D scenes to be rendered are of high resolution and frame rate, these scenes will be offloaded to an edge server for computation. Besides, the metaverse is user-center by design, and human users are always the core. In this work, we introduce a multi-user VR computation offloading over wireless communication scenario. In addition, we devised a novel user-centered deep reinforcement learning approach to find a near-optimal solution. Extensive experiments demonstrate that our approach can lead to remarkable results under various requirements and constraints.Ministry of Education (MOE)Submitted/Accepted versionThis research is partly supported by the Singapore Ministry of Education Academic Research Fund under Grant Tier 1 RG90/22, RG97/20, Grant Tier 1 RG24/20 and Grant Tier 2 MOE2019-T2-1-176; and partly by the NTU-Wallenberg AI, Autonomous Systems and Software Program (WASP) Joint Project

Building a Longitudinal National Integrated Cardiovascular Database - Lessons Learnt From SingCLOUD.

10.1253/circrep.CR-19-0106Circ Rep2133-4