Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.

Federated Learning: Challenges, Methods, and Future Directions

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

Three experimental environments traditionally support network and distributed systems research: network emulators, network simulators, and live networks. The continued use of multiple approaches highlights both the value and inadequacy of each. Netbed, a descendant of Emulab, provides an experimentation facility that integrates these approaches, allowing researchers to configure and access networks composed of emulated, simulated, and wide-area nodes and links. Netbed's primary goals are ease of use, control, and realism, achieved through consistent use of virtualization and abstraction.By providing operating system-like services, such as resource allocation and scheduling, and by virtualizing heterogeneous resources, Netbed acts as a virtual machine for network experimentation. This paper presents Netbed's overall design and implementation and demonstrates its ability to improve experimental automation and efficiency. These, in turn, lead to new methods of experimentation, including automated parameter-space studies within emulation and straightforward comparisons of simulated, emulated, and wide-area scenarios.

An integrated experimental environment for distributed systems and networks

Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.

Towards Federated Learning at Scale: System Design

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A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics. Finally, at least one person identifier from the list is sent to the requester.

Facial recognition with social network aiding

A corpus contains information including text from books and metadata about the books. The book texts mention entities of various types, such as location, date, event, and person entities. An entity importance engine analyzes the information in the corpus to identify the entities mentioned therein, and ranks the entities using query-independent importance scores. The importance scores can be based in part on the contexts in which the entities are mentioned by the books. A presentation engine enables searching of the corpus to identify books satisfying a search query. The presentation engine presents information about a designated book. Entities mentioned in the book are presented in a style selected to enhance comprehension and utility. For example, location entities can be presented on a map with markers showing the locations of the entities. The number of entities presented are limited and ordered based on the query-independent importance scores.

Query-independent entity importance in books

A visual query such as a photograph, a screen shot, a scanned image, a video frame, or an image created by a content authoring application is submitted to a visual query search system. The search system processes the visual query by sending it to a plurality of parallel search systems, each implementing a distinct visual query search process. These parallel search systems may include but are not limited to optical character recognition (OCR), facial recognition, product recognition, bar code recognition, object-or-object-category recognition, named entity recognition, and color recognition. Then at least one search result is sent to the client system. In some embodiments, when the visual query is an image containing a text element and a non-text element, at least one search result includes an optical character recognition result for the text element and at least one image-match result for the non-text element.

David Petrou

Papers

Towards Federated Learning at Scale: System Design

Towards Federated Learning at Scale: System Design

Facial recognition with social network aiding

Query-independent entity importance in books

Architecture for responding to a visual query