We present federated reconnaissance, a new class of learning problems in which distributed clients learn new concepts independently and must be able to communicate that knowledge efficiently. In particular, we propose an evaluation framework and methodological baseline for a system in which each client is expected to learn a growing set of classes and communicate knowledge of those classes efficiently with other clients, such that, after knowledge merging, the clients should be able to accurately discriminate between classes in the superset of classes observed by the set of clients. We compare a range of learning algorithms for this problem and find that prototypical networks are a strong approach in that they are robust to catastrophic forgetting while incorporating new information efficiently. Furthermore, we show that the online averaging of prototype vectors is effective for client model merging and requires only a small amount of communication overhead, memory, and update time per class with no gradient-based learning or hyperparameter tuning. Additionally, to put our results in context, we find that a simple, prototypical network with four convolutional layers significantly outperforms complex, state of the art continual learning algorithms, increasing the accuracy by over 22% (absolute) after learning 600 Omniglot classes and over 33% (absolute) after learning 20 mini-ImageNet classes incrementally. These results have important implications for federated reconnaissance and continual learning more generally by demonstrating that communicating feature vectors is an efficient, robust, and effective means for distributed, continual learning.

Federated Reconnaissance: A New Framework for Distributed Continual Learning

It is well known that Deep Neural Network (DNN) implementations take massive volumes of data to sufficiently train their complex network architectures to perform high accuracy inferencing. The Machine Learning (ML) communities, academic and industry, have created sophisticated models of DNNs trained on millions of data samples to achieve robust classification performance. In the area of image analysis, to address the need for data, numerous efforts have led to standard curated datasets, such as MINST, CIFAR-10, Open Images, and ImageNet. In fact, the ImageNet dataset has become a de-facto image dataset for new algorithm evaluation. In a yearly competition, participants are invited to submit new and novel DNN architectures for assessment. The architectures are trained using the ImageNet database and then evaluated for classification performance. In order of development, some of the well-known winning networks originating from this competition are AlexNet, ZFNet, GoogleNet, VGG Net, ResNet, and Inception V4. As a benefit to the deep learning community, many of the competition submissions are released as open-source for others to use and refine. It is important to recognize that the key to the performance of these networks is that the data input into the network is similar in nature to the data on which the network was trained. Herein is the problem facing many Department of Defense (DoD) ML implementations; the lack of sufficient, operationally relevant data to train and innovate DNN architectures for the DoD community. 
As part of a 2020 Navy Small Business Technology Transfer (STTR) program, Areté and its academic partner, the University of Arizona, are developing an interactive scenario building tool capable of generating realistic synthetic 360° videos in real-time for use in DoD training simulators. This novel capability is accomplished by combining the latest advances in generative adversarial networks (GANs), Areté's expertise in physics-based modeling, and the advanced simulation capabilities of the Unreal Engine. In this talk, Areté will present examples of maritime relevant synthetic data compared to real-world data. Areté will then demonstrate how the data can be used to train DNN architectures directly or through transfer learning and how key technologies are marshaled together.


Using the Unreal Engine as a High Fidelity Simulation for Data Creation


Group Q&A and panel discussion with authors of talks on Autonomy and Edge Computing

**Moderator:** [Michael Tall](https://www.underline.io/speakers/74376-mike-tall)

**Speakers:**  

[Richard Yang ](https://www.underline.io/speakers/80728-richard-yang)

[Ruriko Yoshida ](https://www.underline.io/speakers/77576-ruriko-yoshida)

[Casey Sapp](https://www.underline.io/speakers/74322-casey-sapp)

[Brad Walls](https://www.underline.io/speakers/77594-brad-walls)

[Alisha Sharma](https://www.underline.io/speakers/77588-alisha-sharma)

[Maj. Larry Wigington](https://www.underline.io/speakers/77627-maj.-larry-wigington)

[Douglas Horner](https://www.underline.io/speakers/81600-douglas-horner)


Autonomy and Edge Computing

technical paper

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

NAML 2021

These live Q&A sessions will take place in our Gather.Town lounge. Once you click the gathertown button below, you will need to navigate to the areas located to the left in the lounge. Enter the designated area listed below each Q&A.

**Date & Time:** Wednesday, March 25th 12:30-1:15pm PDT

**Location:** **AREA 1 **

**Title:** AWS DeepRacer Winner Announcement

**Time:** 12:30pm PDT

**Speakers:** 

Deborah Ruttenberg

Hayes Magnuson

[![](https://assets.underline.io/uploads/markdown_image/1/image/c7e88505bb752c494a40c43a7f560168.png)](https://gather.town/app/5IAViMZXC9ZF4E4l/NAML)

**Location:** **AREA 2 **

**Title:** Detecting Cyber Invasions with DNS Analytics & DGA Detection Leveraging Machine Learning

**Time:** 12:45pm PDT

**Speaker:** [Zafer Bilaloglu ](https://www.underline.io/speakers/77584-zafer-bilaloglu)

[![](https://assets.underline.io/uploads/markdown_image/1/image/722aea2c86d9dba9fcf527b54d20e092.png)](https://gather.town/app/5IAViMZXC9ZF4E4l/NAML)

**Location:** **AREA 3 **

**Title:** Using the Unreal Engine as a High Fidelity Simulation for Data Creation

**Time:** 1:00pm PDT

**Speaker:** [Brad Walls](https://www.underline.io/speakers/77594-brad-walls)

[![](https://assets.underline.io/uploads/markdown_image/1/image/e35db3b90d4905c814cf082dceae6913.png)](https://gather.town/app/5IAViMZXC9ZF4E4l/NAML)

Bradley Walls

2

2

SHORT BIO

Presentations

Federated Reconnaissance: A New Framework for Distributed Continual Learning

Using the Unreal Engine as a High Fidelity Simulation for Data Creation

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