We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML systems, primarily for the purpose of personalizing these systems. In recent years, support for an additional federated task has been added: federated learning (FL) of deep neural networks. To our knowledge, only one other system has been described in literature that supports FL at scale. We include comparisons to that system to help discuss design decisions and attached trade-offs. Finally, we describe two specific large scale personalization use cases in detail to showcase the applicability of federated tuning to on-device personalization and to highlight application specific solutions.
This paper is a tutorial review on important issues related to code-division multiple-access (CDMA) systems such as channel capacity, power control, and optimum codes; specifically, we consider optimum overloaded codes that achieve errorless transmission in the absence of noise for the binary and nonbinary cases. A survey of lower and upper bounds for the sum channel capacity of such systems is given in the presence and absence of channel noise. The asymptotic results for the channel capacity are also investigated. The channel capacity, errorless transmission codes, and power estimation for near-far effects are also explored. The emphasis of this tutorial review is on the overloaded CDMA systems.Keywords: Code Division Multiple Access (CDMA), Optimum codes, channel capacity bounds, near-far effects, power control I Introductioncode-division multiple access (CDMA) has been the most important multiple access technology for the 3rd generation GSM and American Cellular systems [1]. Optical CDMA systems have become an alternative multiple access for fiber optics and optical wireless systems [2][3][4].In CDMA systems, each user is assigned a unique code signature that consists of a number of chips. The signature length (also called chip rate) is defined as the number of chips in each signature code. Each user signature is multiplied by the respective data, and the transmitted vectors are added up in the common channel. The resultant vector is then observed at the received user end. In order to decode the received signal, the detector of the received user should know its own unique signature. These codes should be designed such that the cross-correlations between the code of the desired user and the codes of the other users are minimal.For the wireless case, the most well-known binary (Endnote a) code for the synchronous case is Hadamard orthogonal code that is appropriate for fully and underloaded CDMA systems. (Endnote b) But because of bandwidth limitation in the communication systems, we are interested in finding codes that can support more users than the chip rate (overloaded case). In the overloaded case, we cannot use Hadamard codes; Even random codes create interference that cannot be eliminated completely [5][6][7]. Optical orthogonal codes (OOC) [3,8] are not really orthogonal; however, using interference cancelation, we can remove interference completely. Most of the research in the overloaded case is related to code design and multi-access interference (MAI) cancelation in order to decrease the probability of error. Examples of these types of codes are pseudo random spreading [9,10], Welsh Bound Equality (WBE) codes with minimal total square correlation (TSC) [11][12][13][14], OCDMA [15][16][17], Multiple-OCDMA [18], and PN/ OCDMA [19] signature sets. None of the above codes guarantee errorless transmission in the absence of channel noise for overloaded CDMA systems. There are also some codes that are not designed upon cross-correlation and are designed such that they can provide one-to-one transfor...
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