The unique artistic style is crucial to artists’ occupational competitiveness, yet prevailing Art Commission Platforms rarely support style-based retrieval. Meanwhile, the fast-growing generative AI techniques aggravate artists’ concerns about releasing personal artworks to public platforms. To achieve artistic style-based retrieval without exposing personal artworks, we propose FedStyle, a style-based federated learning crowdsourcing framework. It allows artists to train local style models and share model parameters rather than artworks for collaboration. However, most artists possess a unique artistic style, resulting in severe model drift among them. FedStyle addresses such extreme data heterogeneity by having artists learn their abstract style representations and align with the server, rather than merely aggregating model parameters lacking semantics. Besides, we introduce contrastive learning to meticulously construct the style representation space, pulling artworks with similar styles closer and keeping different ones apart in the embedding space. Extensive experiments on the proposed datasets demonstrate the superiority of FedStyle.

Personalized Federated Learning (FL) handles the data heterogeneous problem by tailoring local models for each distributed data owner. Previous studies first train a highly-adaptable global model and then transfer it for personalization. However, the additional training aggravates burden of resource-limited end devices. Training a personalized local sub-network is a promising efficient solution. It normally prunes the global model by parameters’ scalar magnitude. In this paper, we found that the vector magnitude, i.e. the parameter stability, could further promote personalized FL. Driven by the local data characteristics, the values of some model parameters are hardly changed in their updates. But they consume the same resources as the changed ones. Thus, we propose Star-PFL, a STability-AwaRe algorithm for efficient FL Personalization. In Star-PFL, the data owner focuses on training non-stabilized parameters, and decreases the resource wastes on stabilized ones. Experimental results on two real-world biomedical datasets demonstrate that Star-PFL improves the accuracy (3.1%↑) and decreases the resource costs (communication 36.3%↓, computation 18.3%↓) than 5 typical baselines. The code is available at https://github.com/Guoyeting/Star-PFL.

As a decentralized training paradigm, Federated learning (FL) promises data privacy by exchanging model parameters instead of raw local data. However, it is still impeded by the resource limitations of end devices and privacy risks from the ‘curious’ cloud. Yet, existing work predominately ignores that these two issues are non-orthogonal in nature. In this article, we propose a joint design (i.e., AHFL) that accommodates both the efficiency expectation and privacy protection of clients towards high inference accuracy. Based on a cloud-edge-end hierarchical FL framework, we carefully offload the training burden of devices to one proximate edge for enhanced efficiency and apply a two-level differential privacy mechanism for privacy protection. To resolve the conflicts of dynamical resource consumption and privacy risk accumulation, we formulate an optimization problem for choosing configurations under correlated learning parameters (e.g., iterations) and privacy control factors (e.g., noise intensity). An adaptive algorithmic solution is presented based on performance-oriented resource scheduling, budget-aware device selection, and adaptive local noise injection. Extensive evaluations are performed on three different data distribution cases of two real-world datasets, using both a networked prototype and large-scale simulations. Experimental results show that AHFL relieves the end's resource burden (w.r.t. computation time 8.58%$\downarrow$, communication time 59.35%$\downarrow$and memory consumption 43.61%$\downarrow$) and has better accuracy (6.34%$\uparrow$) than 3 typical baselines under the limited resource and privacy budgets. The code for our implementation is available athttps://github.com/Guoyeting/AHFL.

This paper explores resource allocation strategy in the Baidu Over The Edge system to enable mobile edge computing (MEC) datacenters to effectively support cloud-native services downstream to the network edge. There are many challenges to this issue. First, MEC datacenters are resource-constrained to fully meet resource demands. Second, previous works regard the resource requirements of each service as an indivisible unit, resulting in idle MEC resources, even if the resources can meet the demands of some microservices decoupled by the service. Third, they are confined to optimize the allocation for a single slot, failing to adapt to the dynamic demands. To improve resource utilization, we propose performability-aware resource allocation (PARA), a PARA on the edges for cloud-native services. It takes microservices as the unit of resource allocation and allows services to perform with degraded services when only part of microservices' demands are met. It also considers dependency among microservices, dynamic resource requirements, and resource supply characteristics of MEC and cloud. Performability is a unified performance-reliability measure for evaluating such degradable systems. To maximize the long-term overall performability, we model the resource optimization problem and then develop an online greedy heuristic algorithm. The algorithm predicts services' resource demands and then adapts the online allocation. The experimental results show that PARA reduces the reallocation overhead by 47.7%–53.6%, and improves the long-term overall performability by 23.14%–43.25% of existing state-of-the-art works.

The emerging federated learning (FL) paradigm allows multiple distributed devices to cooperatively train models in parallel with the raw data retained locally. The local-computed parameters will be transferred to a centralized server for aggregation. However, the vanilla aggregation method ignores the heterogeneity of the distributed data, which may lead to slow convergence and low training efficiency. Yet, existing data scheduling and improved aggregation methods either incur privacy concerns or fail to consider the fine-grained heterogeneity. We propose FedCav, a contribution-aware model aggregation algorithm that differentiates the merit of local updates and explicitly favors the model-informed contributions. The intuition is that the local data showing higher inference loss is likely to facilitate better performance improvement. To this end, we design a novel global loss function with explicit optimization preference on informative local updates, theoretically prove its convex property, and use it to regulate the gradient descent process iteratively. Additionally, we propose to identify abnormal updates with fake loss by auditing historic local training statistics. The results of extensive experiments demonstrate that FedCav needs fewer training rounds (~34%) for convergence and achieves better inference accuracy (~2.4%) than the baselines (i.e., FedAvg and FedProx). We also observe that FedCav can actively mitigate the model replacement attacks with agile recovery capability towards the aggregation.

With the prosperity of artificial intelligence, neural networks have been increasingly applied in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile wearable devices, widely adopted by hospitals and health organizations, serve as emerging sources of medical data and participate in the training of neural network models for accurate model inference. Since the medical data are privacy-sensitive and non-shareable, federated learning has been proposed to train a model across decentralized data, which involves each mobile device running a training task with its own data in parallel. However, due to the ever-increasing size and complexity of modern neural network models, it becomes inefficient, and may even infeasible, to perform training tasks on wearable devices that are resource-constrained. In this paper, we propose a FEderated Edge Learning system, FEEL, for efficient privacy-preserving mobile healthcare. Specifically, we design an edge-based training task offloading strategy to improve the training efficiency. Further, we build our system on the basis of federated learning to make use of distributed user data to improve the inference performance. In addition, during model training, we provide a differential privacy scheme to strengthen the privacy protection. A prototype system has been implemented to evaluate the training efficiency, inference performance and noise sensitivity, respectively. And the results have demonstrated that our proposal could train models in an efficient and privacy-preserving way.

In the aftermath of earthquakes, floods, and other disasters, photos are increasingly playing more significant roles, such as finding missing people and assessing disasters, in rescue and recovery efforts. These disaster photos are taken in real time by the crowd, unmanned aerial vehicles, and wireless sensors. However, communications equipment is often damaged in disasters, and the very limited communication bandwidth restricts the upload of photos to the cloud center, seriously impeding disaster rescue endeavors. Based on edge computing, we propose Echo, a highly time-efficient disaster rescue framework. By utilizing the computing, storage, and communication abilities of edge servers, disaster photos are preprocessed and analyzed in real time, and more specific visuals are immensely helpful for conducting emergency response and rescue. This article takes the search for missing people as a case study to show that Echo can be more advantageous in terms of disaster rescue. To greatly conserve valuable communication bandwidth, only significantly associated images are extracted and uploaded to the cloud center for subsequent facial recognition. Furthermore, an adaptive photo detector is designed to utilize the precious and unstable communication bandwidth effectively, as well as ensure the photo detection precision and recall rate. The effectiveness and efficiency of the proposed method are demonstrated by simulation experiments.