Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e. requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol

KMS States, Entropy and the Variational Principle in full C*-dynamical systems

To any periodic, unital and full C*-dynamical system (A, \alpha, R) an invertible operator s acting on the Banach space of trace functionals of the fixed point algebra is canonically associated. KMS states correspond to positive eigenvectors of s. A Perron-Frobenius type theorem asserts the existence of KMS states at inverse temperatures equal the logarithms of the inner and outer spectral radii of s (extremal KMS states). Examples arising from subshifts in symbolic dynamics, self-similar sets in fractal geometry and noncommutative metric spaces are discussed. Certain subshifts are naturally associated to the system and the relationship between their topological entropy and inverse temperatures of extremal KMS states are given. Noncommutative shift maps are considered. It is shown that their entropy is bounded by the sum of the entropy of the associated subshift and a suitable entropy computed in the homogeneous subalgebra. Examples are discussed among Matsumoto algebras associated to certain non finite type subshifts. The CNT entropy is compared to the classical measure-theoretic entropy of the subshift. A noncommutative analogue of the classical variational principle for the entropy of subshifts is obtained for the noncommutative shift of certain Matsumoto algebras. More generally, a necessary condition is discussed. In the case of Cuntz-Krieger algebras an explicit construction of the state with maximal entropy from the unique KMS state is done.Comment: 52 pages, AMSTeX. An error in Prop. 7.3 v1 has been corrected, and related text in sections 7-9 has been modified. References added. Abstract modifie

Future Person Localization in First-Person Videos

We present a new task that predicts future locations of people observed in first-person videos. Consider a first-person video stream continuously recorded by a wearable camera. Given a short clip of a person that is extracted from the complete stream, we aim to predict that person's location in future frames. To facilitate this future person localization ability, we make the following three key observations: a) First-person videos typically involve significant ego-motion which greatly affects the location of the target person in future frames; b) Scales of the target person act as a salient cue to estimate a perspective effect in first-person videos; c) First-person videos often capture people up-close, making it easier to leverage target poses (e.g., where they look) for predicting their future locations. We incorporate these three observations into a prediction framework with a multi-stream convolution-deconvolution architecture. Experimental results reveal our method to be effective on our new dataset as well as on a public social interaction dataset.Comment: Accepted to CVPR 201

Captive Elephants in Japan: Census and History

A total of 172 elephants was maintained in 68 institutions in Japan as of 1984. The elephants were distributed as follows: 89 (3/76)* Asian elephants (Elephas maximus) and 83 (19/64) African elephants (Loxodonta africana). The 68 institutions included 64 public and private zoos parks and others plus 4 circuses which held from one (1) elephant to twelve (12) elephants. Results of the survey also showed that 32 (4/28) elephants had been with their current owners for more than 20 years. The oldest elephant in Japan was held by Ueno Zoo, Tokyo; she ( Indira ) was 49 years old as of 1983 when she died