NYAI #26 Federated Learning: Machine Learning on Edge Devices

Federated Learning: Machine Learning on Edge Devices

Federated learning enables us to build machine learning models using data collected by edge devices like smartphones and IoT devices, without moving data off the device. This minimizes concerns about privacy, data regulation, bandwidth, and storage while providing similar results as centralized models. Examples include predictive text on cell phones, a person’s engagement with their own photos, and machine learning in the browser applied to corporate text archives such as a team Slack or Google Drive, and ML on low-powered field devices in energy, agriculture, and logistics.

The principles of data minimization established by the GDPR and the prevalence of smart sensors make these use cases more common, and the advantages of federated learning more compelling. In this talk, we’ll cover the algorithmic solutions and product opportunities.

[slideshare id=124946222&doc=nyaifederatedlearningtalk-181204172707&w=650&h=500]
About the Speakers

Alice Albrecht is a research engineer at Cloudera Fast Forward Labs.  She spends her days researching the latest and greatest in machine learning and artificial intelligence and bringing that knowledge to working prototypes and delivering concrete advice for clients.  Prior to joining Fast Forward Labs, Alice worked in both finance and technology companies as a practicing data scientist, data science leader, and – most recently – a data product manager. In addition to teaching machines to do cool things, Alice is passionate about mentoring and helping others grow in their careers.  Alice holds a PhD from Yale in cognitive neuroscience where she studied how humans summarize sensory information from the world around them and the neural substrates that underlie those summaries.

Dauna Williams, of counsel with Burgher Gray, LLP in New York City, heads BurgherGray’s Technology, Privacy, and Intellectual Property team.  She has over 30 year years of experience, leading both Fortune 50 in-house and in law firm intellectual property and technology practices. Dauna has handled complex commercial transactions, intellectual property, technology, privacy, data protection, advertising, marketing, legal risk management and compliance, and corporate formation and governance matters. With her diversity of experience, she has led multiple strategic technologies and intellectual property initiatives for global financial services companies, as well as served as outside general counsel for startups and small-to-medium enterprises.  Dauna has an A.B. from Brown University and a J.D. from Harvard University.

Sabtain is a product manager at IBM Watson, leading the Visual Recognition service. An Alumnus of Michigan State University, he has a background in product, user experience, design, finance and is an avid reader.

NYAI (New York Artificial Intelligence)

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We understand the profound impact AI could have on the world, and believe inclusive conversations are the best way to gain consensus and responsibly guide technical progress in a way that benefits all human beings.

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