Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new algorithmic paradigm for Federated Learning with fundamentally different communication properties, emerged. FD methods leverage ensemble distillation techniques and exchange model outputs, presented as soft labels on an unlabeled public data set, between the central server and the participating clients. While for conventional Federated Learning algorithms, like Federated Averaging (FA), communication scales with the size of the jointly trained model, in FD communication scales with the distillation data set size, resulting in advantageous communication properties, especially when large models are trained. In this work, we investigate FD from the perspective of communication efficiency by analyzing the effects of active distillation-data curation, soft-label quantization and delta-coding techniques. Based on the insights gathered from this analysis, we present Compressed Federated Distillation (CFD), an efficient Federated Distillation method. Extensive experiments on Federated image classification and language modeling problems demonstrate that our method can reduce the amount of communication necessary to achieve fixed performance targets by more than two orders of magnitude, when compared to FD and by more than four orders of magnitude when compared with FA.

Neural network forms the foundation of deep learning and numerous AI applications. Classical neural networks are fully connected, expensive to train and prone to overfitting. Sparse networks tend to have convoluted structure search, suboptimal performance and limited usage. We proposed the novel uniform sparse network (USN) with even and sparse connectivity within each layer. USN has one striking property that its performance is independent of the substantial topology variation and enormous model space, thus offers a search-free solution to all above mentioned issues of neural networks. USN consistently and substantially outperforms the state-of-the-art sparse network models in prediction accuracy, speed and robustness. It even achieves higher prediction accuracy than the fully connected network with only 0.55% parameters and 1/4 computing time and resources. Importantly, USN is conceptually simple as a natural generalization of fully connected network with multiple improvements in accuracy, robustness and scalability. USN can replace the latter in a range of applications, data types and deep learning architectures. We have made USN open source at https://github.com/datapplab/sparsenet.

"Girls of Steel Robotics (featured 2014) was founded in 2010 at Carnegie Mellon University's Field Robotics Center as FRC Team 3504. The organization now serves multiple FIRST robotics teams offering STEM opportunities for people of all ages. Since 2019, Girls of Steel also organizes FIRST Ladies, an online community for anyone involved in FIRST robotics programs who supports girls and women in STEM. Their mission statement reflects their commitment to empowering everyone for success in STEM: "Girls of Steel empowers everyone, especially women and girls, to believe they are capable of success in STEM." One is a PhD student in Robotics at CMU, two are working as engineers, and one is a computer science teacher.

Artificial intelligence (AI) and machine learning, which is a subset of AI, are opening new opportunities in essentially all businesses, in addition to making frequently used equipment more capable. As anyone might expect, AI and machine learning are frequently applied to robots to improve them. Robots used in the industrial sector can assist organizations in completing more things with fewer blunders. Obviously, safety is key while adding robots in the work environment which is the reason some AI robotics organizations are creating solutions where robots can comprehend what's in their current environment and respond likewise. Veo Robotics has an industrial robotics system that consolidates computer vision, AI and sensors.

Gerry Bayne: Welcome to EDUCAUSE Exchange, where we focus on a single question from the higher ed IT community and hear advice, anecdotes, best practices, and more. Students with disabilities are a vulnerable population in higher education. Yet the real percentage is likely higher, given that many choose not to disclose their disability to their institutions. Students with disabilities experience barriers to education that many other students do not. And they can have both visible and invisible needs. Their dropout rates are substantially higher and their graduation rates are significantly lower than those of non-disabled students.

Finland has released a free online artificial intelligence (AI) course which aims to educate public administration, businesses and the general public about the technology and to consider what it should be used for. The Ethics of AI course offered by the University of Helsinki has been designed in a partnership with the cities of Helsinki, Amsterdam and London as well as Finland's Ministry of Finance. Questions pertaining to the ethics of AI are topical, as many people are already making ethical choices in their work, for example, on data use. The course sets out to help them understand what the ethical use of artificial intelligence means and what it requires from both society and individuals. "These questions include how our data is used, who is responsible for decisions made by computers and whether, say, facial recognition systems are used in a way that acknowledges human rights. In a broader sense, it's also about how we wish to utilise advancing technical solutions," said Anna-Mari Rusanen, course coordinator for Ethics of AI.

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Teachable Machine is an experiment from Google to bring a no-code and low-code approach to training AI models. Anyone with a modern browser and webcam can quickly train a model with no prior knowledge or experience with AI. Teachable Machine is not exactly new. It was initially launched in 2017 and got revamped in 2019 with additional capabilities, including saving the model to Google Drive and exporting it to other applications. The community behind the project is continuously making it better. It has become so popular that education researcher Blakeley H. Payne and her teammates have been using Teachable Machine as part of an open-source curriculum that teaches middle-schoolers about AI through a hands-on learning experience.

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Elements of AI is a free online course that launched in Ireland last month and is designed to be accessible from the beginning. At Future Human last month, it was announced that the University of Helsinki has teamed up with University College Cork to bring artificial intelligence "into the sitting rooms and kitchens of Irish homes". This will be through Elements of AI, a free online course designed and organised by the University of Helsinki and Finnish tech company Reaktor. Prof Teemu Roos of the University of Helsinki told the Future Human audience that the course was designed to be accessible from the beginning, starting with what artificial intelligence is and how we encounter it every day, before moving onto how it works and what the basic principles are. "There's no programming in the course, you don't have to know any programming to get started and complete the course. There's hardly any mathematics," he said.