Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available at https://github.com/llmir/FedLPPA.

Multimodal federated learning enhances model performance by integrating data from different modalities. However, this integration also introduces new privacy leakage risk associated with cross-modal correlation. Existing differential privacy methods are primarily designed for single-modal data, making them ineffective in multimodal scenarios, while noise addition often compromises data utility. In this paper, we propose an Embedding Multimodal Fusion Federated Learning Approach, called EM2FL. This approach leverages horizontal multimodal federated learning to collaboratively train global models across multiple data platforms without exposing raw private data. Instead of applying differential privacy to each modality-specific encoder separately, we implement it on the mixed embedding module that combines multiple modalities. We tested our approach on 3 distinct models and validated its effectiveness for the combination of image and user-item pair modalities. Experimental results show that our method improves performance by 10-20% compared to traditional differential privacy methods while maintaining a similar level of privacy protection. This demonstrates that EM2FL effectively balances privacy and performance through selective noise addition.

Machine Learning (ML) has become ubiquitous, fueling data-driven applications across various organizations. Contrary to the traditional perception of ML in research, ML workflows can be complex, resource-intensive, and time-consuming. Expanding an ML workflow to encompass a wider range of data infrastructure and data types may lead to larger workloads and increased deployment costs. Currently, numerous workflow engines are available (with over ten being widely recognized). This variety poses a challenge for end-users in terms of mastering different engine APIs. While efforts have primarily focused on optimizing ML Operations (MLOps) for a specific workflow engine, current methods largely overlook workflow optimization across different engines. In this work, we design and implement Couler, a system designed for unified ML workflow optimization in the cloud. Our main insight lies in the ability to generate an ML workflow using natural language (NL) descriptions. We integrate Large Language Models (LLMs) into workflow generation, and provide a unified programming interface for various workflow engines. This approach alleviates the need to understand various workflow engines' APIs. Moreover, Couler enhances workflow computation efficiency by introducing automated caching at multiple stages, enabling large workflow auto-parallelization and automatic hyperparameters tuning. These enhancements minimize redundant computational costs and improve fault tolerance during deep learning workflow training. Couler is extensively deployed in real-world production scenarios at Ant Group, handling approximately 22k workflows daily, and has successfully improved the CPU/Memory utilization by more than 15% and the workflow completion rate by around 17%.

In recent years, desktop virtualization trends to be a new extension of virtualization framework. The existing desktop virtualization systems suffer performance degradation in terms of response time and video quality. However, previous remote access approaches are designed for standalone architectures and require semantic information which is not transparent to OS. So they are not feasible in desktop virtualization systems. In this paper, we propose SRIDesk, a Streaming based Remote Interactivity architecture for Desktop virtualization system. SRIDesk resides in the host through intercepting virtual display device, which is transparent to guest OS and its applications. SRIDesk integrates server-push streaming mechanism with H.264 encoder into virtualization system, which provides high quality display with low bandwidth consumption and low latency of interaction. We have implemented the SRIDesk prototype in a KVM system. Experimental results show that SRIDesk has low CPU-load, low bandwidth and good scalability. We compared SRIDesk with other popular platforms, including X, VNC, RDP and THiNC. SRIDesk outperformed other systems in bandwidth with no more than 2Mbps and 94% video quality. SRIDesk also achieved lowest latency in WAN environment among all systems.