Our federated learning framework brings intelligence to every device, enabling secure, scalable AI from the cloud to the edge, all while keeping your data safe.
Machine Learning (ML)Federated Learning (FL).AI running locally on IoT Edge devices is called Edge AI. Federated Learning (FL) is a Machine Learning (ML) technique that builds upon the concept of distributed computing and preserves data privacy while still supporting trainable AI models. This ...
Wang X, Han Y, Leung VC et al (2020) Edge AI: convergence of edge computing and artificial intelligence, 1st edn. Springer, Singapore Book Google Scholar Zhao Y, Hryniewicki MK (2018) XGBOD: improving supervised outlier detection with unsupervised representation learning. In: International join...
Yu, H., Liu, Z., Liu, Y., et al.: A fairness-aware incentive scheme for federated learning. In: Proceedings of the 2020 AAAI/ACM Conference on AI, Ethics, and Society (AIES'20), New York, USA (2020) Zhan, Y., Zhang, J., Hong, Z., et al.: A survey of incentive mechanism...
for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at ...
orchestrate and sustain the network infrastructure ranging from topology management (edge site orchestration), to data and service provisioning. Among such techniques,AIandMachine Learning(ML) are considered as a key solution for many challenges. The marriage ofedge computingand AI established a new re...
There’s nothing stopping you from combining several different features and signals as the input to your AI algorithms. For example, you could calculate several moving averages of a time series over several different windows and pass them all into a machine learning model together. There are no...
题目:Convergence of Edge Computing and Deep Learning: A Comprehensive Survey 摘要:来自工厂和社区的无处不在的传感器和智能设备可保证大量数据,不断增加的计算能力正在推动计算和服… 聂聂 OPEN AI LAB Edge AI推理框架Tenigne全解读 最近,国内的人工智能(AI)开源生态突然热闹了起来,这厢清华大学刚开源了一个强...
In this chapter, we study semi-supervised edge learning, where the model is first initialized via resource-efficient FL across many edge devices and then personalized for an edge device with limited data samples. In particular, we delve into adaptive device selection and scheduling problem for ...
In EC, FL enables large-scale device collaboration for training AI models in a privacy-preserving manner. However, the scale of edge devices involved in FL is huge, and the performance and computational power of hardware devices may vary greatly. Meanwhile, the geographic distribution of edge dev...