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

Approach to Rectal Cancer Surgery

Rectal cancer is a distinct subset of colorectal cancer where specialized disease-specific management of the primary tumor is required. There have been significant developments in rectal cancer surgery at all stages of disease in particular the introduction of local excision strategies for preinvasive and early cancers, standardized total mesorectal excision for resectable cancers incorporating preoperative short- or long-course chemoradiation to the multimodality sequencing of treatment. Laparoscopic surgery is also increasingly being adopted as the standard rectal cancer surgery approach following expertise of colorectal surgeons in minimally invasive surgery gained from laparoscopic colon resections. In locally advanced and metastatic disease, combining chemoradiation with radical surgery may achieve total eradication of disease and disease control in the pelvis. Evidence for resection of metastases to the liver and lung have been extensively reported in the literature. The role of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for peritoneal metastases is showing promise in achieving locoregional control of peritoneal dissemination. This paper summarizes the recent developments in approaches to rectal cancer surgery at all these time points of the disease natural history

Weak expression of cyclooxygenase-2 is associated with poorer outcome in endemic nasopharyngeal carcinoma: analysis of data from randomized trial between radiation alone versus concurrent chemo-radiation (SQNP-01)

<p>Abstract</p> <p>Background</p> <p>Over-expression of cyclooxygenase-2 (COX-2) enzyme has been reported in nasopharyngeal carcinoma (NPC). However, the prognostic significance of this has yet to be conclusively determined. Thus, from our randomized trial of radiation versus concurrent chemoradiation in endemic NPC, we analyzed a cohort of tumour samples collected from participants from one referral hospital.</p> <p>Methods</p> <p>58 out of 88 patients from this institution had samples available for analysis. COX-2 expression levels were stratified by immunohistochemistry, into negligible, weak, moderate and strong, and correlated with overall and disease specific survivals.</p> <p>Results</p> <p>58% had negligible or weak COX-2 expression, while 14% and 28% had moderate and strong expression respectively. Weak COX-2 expression conferred a poorer median overall survival, 1.3 years for weak versus 6.3 years for negligible, 7.8 years, strong and not reached for moderate. There was a similar trend for disease specific survival.</p> <p>Conclusion</p> <p>Contrary to literature published on other malignancies, our findings seemed to indicate that over-expression of COX-2 confer a better prognosis in patients with endemic NPC. Larger studies are required to conclusively determine the significance of COX-2 expression in these patients.</p

Evaluation of Best Supportive Care and Systemic Chemotherapy as Treatment Stratified according to the retrospective Peritoneal Surface Disease Severity Score (PSDSS) for Peritoneal Carcinomatosis of Colorectal Origin

Background: We evaluate the long-term survival of patients with peritoneal carcinomatosis (PC) treated with systemic chemotherapy regimens, and the impact of the of the retrospective peritoneal disease severity score (PSDSS) on outcomes. Methods: One hundred sixty-seven consecutive patients treated with PC from colorectal cancer between years 1987-2006 were identified from a prospective institutional database. These patients either received no chemotherapy, 5-FU/Leucovorin or Oxaliplatin/Irinotecan-based chemotherapy. Stratification was made according to the retrospective PSDSS that classifies PC patients based on clinically relevant factors. Survival analysis was performed using the Kaplan-Meier method and comparison with the log-rank test. Results: Median survival was 5 months (95% CI, 3-7 months) for patients who had no chemotherapy, 11 months (95% CI, 6-9 months) for patients treated with 5 FU/LV, and 12 months (95% CI, 4-20 months) for patients treated with Oxaliplatin/Irinotecan-based chemotherapy. Survival differed between patients treated with chemotherapy compared to those patients who did not receive chemotherapy (p = 0.026). PSDSS staging was identified as an independent predictor for survival on multivariate analysis [RR 2.8 (95%CI 1.5-5.4); p < 0.001]. Conclusion: A trend towards improved outcomes is demonstrated from treatment of patients with PC from colorectal cancer using modern systemic chemotherapy. The PSDSS appears to be a useful tool in patient selection and prognostication in PC of colorectal origin