A survey on security and privacy of federated learning

Federated Learning (FL) has been foundational in improving the performance of a wide range of applications since it was first introduced by Google. Some of the most prominent and commonly used FL-powered applications are Android’s Gboard for predictive text and Google Assistant. FL can be defined as a setting that makes on-device, collaborative Machine Learning possible. A wide range of literature has studied FL technical considerations, frameworks, and limitations with several works presenting a survey of the prominent literature on FL. However, prior surveys have focused on technical considerations and challenges of FL, and there has been a limitation in more recent work that presents a comprehensive overview of the status and future trends of FL in applications and markets. In this survey, we introduce the basic fundamentals of FL, describing its underlying technologies, architectures, system challenges, and privacy-preserving methods. More importantly, the contribution of this work is in scoping a wide variety of FL current applications and future trends in technology and markets today. We present a classification and clustering of literature progress in FL in application to technologies including Artificial Intelligence, Internet of Things, blockchain, Natural Language Processing, autonomous vehicles, and resource allocation, as well as in application to market use cases in domains of Data Science, healthcare, education, and industry. We discuss future open directions and challenges in FL within recommendation engines, autonomous vehicles, IoT, battery management, privacy, fairness, personalization, and the role of FL for governments and public sectors. By presenting a comprehensive review of the status and prospects of FL, this work serves as a reference point for researchers and practitioners to explore FL applications under a wide range of domains. 

Federated learning review: Fundamentals, enabling technologies, and future applications

Methods and apparatus for selectively caching (and de-caching) video content in network so as to reduce content transformation requirements and also cache storage requirements. In one embodiment, a content caching controller associated with a content server differentiates content requests based on content attributes such as the requested codec format (e.g., MPEG or Windows Media), resolution, bitrate, and/or encryption type or security environment. If the content requested by a user is not available with the requested attribute(s), the content server transfers to content to the user by first transforming it. The content server also speculatively caches the transformed content locally, so that a future request for the same content with the same attributes can be filled by transferring without the intermediate transformation step. The controller allows the network operator to optimize use of available storage and transcoding resources.

Methods and apparatus for content caching in a video network

Methods and apparatus for selecting and delivering advertising or promotional content based on, eg, the attributes of a desired demographic In one embodiment, the invention enhances advertising or promotional penetration into a particular demographic of a network subscriber pool by correlating the advertising/promotion (and even the contextual program content) with specific users or groups of users within the pool This approach decouples the advertising or promotion from any given program or lineup, and gives the network operator flexibility in deciding where, when, and how often to insert advertisements or promotions in order to achieve the desired result Dynamic network architectures such as broadcast switched architectures can also be leveraged to further enhance flexibility and targeting precision In effect, the network advertising or promotion model is made results-driven (versus program-centric) Subscriber privacy is also optionally maintained, thus ensuring that stored data is not traceable to a specific user

Methods and apparatus for user-based targeted content delivery

Notifying users of mobile communication devices, which may include providing options for response to notifications. A component of a communication service provider system receives an indication of a notification for a user of a mobile communication device. The notification may be received from a source internal or external to the communication service provider system. A component of the mobile communication service provider formats the notification so that it is compatible with the mobile communication device of the user and sends the notification to the mobile communication device of the user. The notification may include one or more options to respond to the notification using the mobile communication device. The notification may, for example, pertain to minutes-of-use in a mobile communication service plan, or to other information, including notifications from outside service providers such as airlines, banks, restaurants, hotels, libraries, credit card companies, etc.

Mobile device notification with options

Understanding user behavior and adapting to it has been an important focus area for applications. That adaptation is commonly called Personalization. Personalization has been sought after in gaming, personal assistants, dialogue managers, and other popular application categories. One of the challenges of personalization methods is the time they take to adapt to the user behavior or reactions. This sometimes is detrimental to user experience. The contribution of this work is twofold: (1) showing the applicability of granular (per user) personalization through the use of reinforcement learning, and (2) proposing a novel mitigation strategy to decrease the personalization time, through federated learning. To our knowledge, this paper is among the first to present an overall architecture for federated reinforcement learning (FRL), which includes the grouping policy, the learning policy, and the federation policy. We demonstrate the efficacy of the proposed architecture on a non-player character in the Atari game Pong, and scale the implementation across 3, 4, and 5 users. We demonstrate the success of the proposal through achieving a median improvement of ~17% on the personalization time.

Federated Reinforcement Learning for Fast Personalization

A method of initiating a wireless communication session with a communication network is provided. A cost estimate for the communication session is determined based on a quality of service level and a quantity of information to be communicated. The communication session is initiated when the cost estimate is accepted. In one embodiment, the user may select a second quality of service level for the communication session when the first cost estimate is not accepted and is provided a second cost estimate for the second quality of service level. In yet another embodiment, a communication session is initiated when there is sufficient balance available to the wireless communication device or a user's smart card for a communication session at a quality of service level selected by the user. In one embodiment, the method may be performed in accordance with a packet radio service network providing packet data services.

System and method for providing cost of quality of service levels in a wireless communication device

A wireless communication device includes an applications processor to operate one or more personal information management (PIM) applications. Upon initial registration or when transiting to a new location, a baseband processor of the device automatically derives the local time using a time-zone database and current location information received from the communication network. The PIM applications operating within the device are updated with the local time. The device may receive location-based services such as advertisements that depend on the current location.

Wireless communication device and method for automatic time updates in personal information management applications

SPEC has recently released SPECpower_ssj2008, the first industry benchmark which measures performance and power of volume server class computers using graduated load levels. In this paper, we present a brief overview and an initial characterization of this benchmark by measuring the system resource utilization with the aid of processor monitoring events at graduated load levels and by comparing the sensitivity of final metric and other related data between various configurations consisting of hardware changes as well as software changes on Quad Core Intel Xeon processor based servers. Even though this is early data from a specific platform and OS, it still validates many expected patterns and opens exciting new opportunities for researchers to investigate specific areas as well as in-depth characterization as a next step.

Workload Characterization of the SPECpower_ssj2008 Benchmark

In embodiments, apparatuses, methods and storage media are described that are associated with remote control of media devices. In embodiments, a remote control and a content player may be configured to facilitate a secured pairing process. The remote control may be configured to send non-secured signals (such as through an IR transmitter) as well as send and receive secured signals (such as via Bluetooth™ using an RF transceiver). The remote control, in response to being turned on or asked to pair, may send a pairing request to the player that includes an identifier for the remote control and which identifies the remote control as an eligible pairing device without requiring separate entry of a security code by a user. The remote control and player may then perform a pairing process without requiring additional action on the part of a user. Other embodiments may be described and/or claimed.

Automated pairing of remote devices

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