Federated Learning

collaborative AI without centralizing data

Motivation: Every project has a beautiful feature showcase page. It’s easy to include images in a flexible 3-column grid format. Make your photos 1/3, 2/3, or full width.

To give your project a background in the portfolio page, just add the img tag to the front matter like so:

layout: page
title: project
description: a project with a background image
img: /assets/img/12.jpg
Caption photos easily. On the left, a road goes through a tunnel. Middle, leaves artistically fall in a hipster photoshoot. Right, in another hipster photoshoot, a lumberjack grasps a handful of pine needles.
This image can also have a caption. It's like magic.

You can also put regular text between your rows of images. Say you wanted to write a little bit about your project before you posted the rest of the images. You describe how you toiled, sweated, bled for your project, and then… you reveal it’s glory in the next row of images.

You can also have artistically styled 2/3 + 1/3 images, like these.

The code is simple. Just wrap your images with <div class="col-sm"> and place them inside <div class="row"> (read more about the Bootstrap Grid system). To make images responsive, add img-fluid class to each; for rounded corners and shadows use rounded and z-depth-1 classes. Here’s the code for the last row of images above:

<div class="row justify-content-sm-center">
    <div class="col-sm-8 mt-3 mt-md-0">
        {% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
    <div class="col-sm-4 mt-3 mt-md-0">
        {% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}

Funding Sources

  1. PI, Collaborative Research: CNS Core: Medium: Towards Federated Learning over 5G Mobile Devices: High Efficiency, Low Latency, and Good Privacy (CNS-2106761), National Science Foundation, $250,000, 10/2021 - 09/2025.

Selected Publications

  1. R. Hu, Y. Gong, and Y. Guo, “Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization,” The 30th International Joint Conference on Artificial Intelligence (IJCAI-21), August 21–26, 2021.

  2. R. Hu, Y. Guo, and Y. Gong, “Concentrated Differentially Private Federated Learning with Performance Analysis,” IEEE Open Journal of the Computer Society (OJ-CS), vol. 2, pp. 276-289, July 2021, DOI: 10.1109/OJCS.2021.3099108.

  3. M. Wu, D. Ye, J. Ding, Y. Guo, R. Yu, and M. Pan, “Incentivizing Differentially Private Federated Learning: A Multi-Dimensional Contract Approach,” IEEE Internet of Things Journal (IoT-J), vol. 8, no. 13, pp. 10639-10651, July 2021.

  4. R. Hu, Y. Guo, H. Li, Q. Pei, and Y. Gong, “Personalized Federated Learning with Differential Privacy,” IEEE Internet of Things Journal (IoT-J), vol. 7, no. 10, pp. 9530 - 9539, October 2020.

  5. R. Hu, Y. Guo, H. Li, Q. Pei, and Y. Gong, “Privacy-Preserving Personalized Federated Learning,” IEEE International Conference on Communications (ICC), Dublin, Ireland, June 7–11, 2020.

  6. Z. Huang, R. Hu, Y. Guo, E. Chan-Tin, and Y. Gong, “DP-ADMM: ADMM-based Distributed Learning with Differential Privacy,” IEEE Transactions on Information Forensics and Security (TIFS), vol. 15, pp. 1002-1012, 2019.