参考 Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
Thus, the device may perform supervised on-device learning based on the remote NLU data. The device may determine differences between the updated speech processing model and an original speech processing model received from the remote system and may send data indicating these differences to the ...
再不进行类似于《EFFICIENT AND ROBUST ASYNCHRONOUS FEDERATED LEARNING WITH STRAGGLERS》中进行动态调整straggler(“掉队者”)本地epoch的前提下,在DAG-FL中,在某一特定时间点,一个节点N接收到straggler的local model的准确率会天然的低于接收到的正常节点的local model, 那么正常的话straggler的transaction会一直被抛弃...
Machine Learning - Deliver On-Device Machine Learning Solutions By Larry O'Brien | Connect(); 2017 You’ve read the headlines: Artificial intelligence and machine learning (AI/ML) technologies are rewriting the benchmarks across a vast swath of hard problems. Whether it’s AlphaGo...
Federated Learning通过device之间传递gradients,不传递data来保证data privacy,但是Deap Leakage可以通过gradients倒推原数据,仍然有数据安全问题。增加Gaussian/laplcian noise来抵御可能会导致模型表现下降,但是梯度压缩可以防止leakage问题。On Device训练的bottleneck是memory,为此有TinyTL的方式,使用fine-tune bias + lite ...
Floating-Point Optimized On-Device Learning Library for the PULP Platform. - pulp-platform/pulp-trainlib
Machine Learning - Deliver On-Device Machine Learning Solutions By Larry O'Brien | Connect(); 2017 You’ve read the headlines: Artificial intelligence and machine learning (AI/ML) technologies are rewriting the benchmarks across a vast swath of hard problems. Whether it’s AlphaGo besting the ...
In deep learning, embeddings are widely used to represent categorical entities such as words, apps, and movies. An embedding layer maps each entity to a unique vector, causing the layer’s memory requirement to be proportional to the number of entities. In the recommendation domain, a given ca...
AI, machine learning, deep learning, autonomous systems and neural networks are not just buzzwords and phrases. Increased compute power, more efficient hardware and robust software, as well as an explosion in sensor data from the Internet of Things — are fueling machine learning, and moving ...
在联邦优化过程中,终端设备基于本机存储的数据进行计算,计算结果用来更新全局模型。假设在网络中有大量的终端设备,每个设备仅有一小部分可用数据,有时可用的数据量甚至小于设备的数量。除此之外,由于不同的用户产生出局的模式不一样,因此可以说任何一个设备中存储的数据样本都不能代表整体数据分布。 作者声称已有的...