Traditional machine learning pipelines require data to be centralized. For data that are collected and stored on multiple edge devices, or for data owned by multiple institutions that must not share their sensitive information, the traditional pipeline becomes impractical due to bandwidth bottlenecks and privacy concerns. Federated learning is a machine learning specialization that addresses these issues and allows for accurate, timely decision making on data that are not centrally available. Rather than aggregating distributed data into one centralized dataset, federated learning assumes that the data remain at the edge. By doing so, federated learning reduces the bandwidth strain over communication lines, allows institutions to share knowledge without giving up their data, and leverages distributed edge computing. In our current studies, we seek to develop a federated algorithm that converges quickly to an accurate model, that is practical for implementation on edge devices with limited processing capabilities, and that is bandwidth efficient. More specifically, we focus on federated optimization with one central server and many clients, and introduce our algorithm called Federated Kronecker-factored quasi-Newton. We are using Kronecker-factorization to make loss-curvature information available cheaply at the edge for federated training in this second-order optimization procedure, which to our knowledge, has not yet been done. We highlight the benefits and challenges of second order optimization in the federated setting and demonstrate preliminary performance curves that show improved model accuracy on smaller amounts of information exchanged and in less training time than some state-of-the-art algorithms. We also show that even though our algorithm is a second-order optimization technique, it is still practical to implement.

Second-Order Federated Optimization

In a cyber defense scenario, it is important to mitigate threats on a system
in an automated and timely manner. While previous work with single-agent
reinforcement learning attempts to accomplish these goals, we improve upon
scalability by introducing a cooperative multi-agent reinforcement learning al-
gorithm. More specically, we implement a Deep Q-Network (DQN) algorithm
with Metropolis consensus weights based on shared action-value function pa-
rameters. Using a cyber simulation, we compare the results of our multi-agent
DQN to the results of a single-agent DQN.

Cooperative Multi-Agent Reinforcement Learning for Cyber Defense

**Moderator:** [Dr. John Reeder](https://www.underline.io/speakers/44023-john-reeder)

**Speakers:**

[Andrew Kan](https://www.underline.io/speakers/77535-andrew-kan)

[George Foster](https://www.underline.io/speakers/77609-george-foster)

[Jonathan Tu](https://www.underline.io/speakers/77559-jonathan-tu)

[Bruce Nagy](https://www.underline.io/speakers/77539-bruce-nagy)


Machine Learning in Operational Environments

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

**Moderator:** John Emanuello

**Speakers:**

[Ahmad Ridley](https://www.underline.io/speakers/77663-ahmad-ridley)

[Elizabeth Gilmour](https://www.underline.io/speakers/77545-elizabeth-gilmour)
     
[Julia Allen](https://www.underline.io/speakers/77564-julia-allen)

[Christianne Izumigawa](https://www.underline.io/speakers/78912-christianne-izumigawa)
        
Roger A. Hallman


Christianne Izumigawa

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Presentations

Second-Order Federated Optimization

Cooperative Multi-Agent Reinforcement Learning for Cyber Defense

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