Although theoretical federated learning (FL) research is growing exponentially, we are far from putting those theories into practice. Over the course of last few years, SymbioticLab has made significant progress in building deployable FL systems, with Oort being the most prominent example. As I discussed in the past, while evaluating Oort, … Continue Reading ››
Tag Archives: Wide-Area Computing
Thanks Cisco for Sponsoring Our FL Research
Look forward to even more work in the context of federated learning and edge AI/ML building on top of FedScale.
Join SymbioticLab if you are excited about building practical federated learning and analytics systems that can be deployed in the wild!
FedScale Wins the Best Paper Award at ResilientFL’2021
Many congratulations to Fan, Yinwei, and Xiangfeng!
Check out FedScale at fedscale.ai
Oort Wins the Distinguished Artifact Award at OSDI’2021. Congrats Fan and Xiangfeng!
Oort, our federated learning system for scalable machine learning over millions of edge devices has received the distinguished artifact award at this year's USENIX OSDI conference!
This is a testament to a lot of hard work put in by Fan and Xiangfeng over the course of last … Continue Reading ››
NSF Award to Expand Our Federated Learning Research!
This collaborative project with Harsha Madhyastha (Michigan) and Aditya Akella (UT Austin) aims to extend and expand our recent forays into federated learning and analytics. Join us in this adventure.
Thanks NSF for supporting our research!
FedScale Released on GitHub
Anyone working on federated learning (FL) has faced this problem at least once: you are reading two papers and they either use very different datasets for performance evaluation or unclear about their experimental assumptions about the runtime environment, or both. They often deal with very small datasets as well. There have been attempts … Continue Reading ››
Thanks Cisco for Sponsoring Our Research
Oort Accepted to Appear at OSDI’2021
Oort's working title was Kuiper.
With the wide deployment of AI/ML in our daily lives, the need for data privacy is receiving more attention in recent years. Federated Learning (FL) is an emerging sub-field of machine learning that focuses on in-situ processing of data wherever it is generated. … Continue Reading ››
Sol and Pando Accepted to Appear at NSDI'2020
With the advent of edge analytics and federated learning, the need for distributed computation and storage is only going to increase in coming years. Unfortunately, existing solutions for analytics and machine learning have focused primarily on datacenter environments. When these solutions are applied to wide-area scenarios, their compute efficiency decreases and storage overhead … Continue Reading ››
Joint Award With CMU on Distributed Storage. Thanks NSF!
This project aims to build on top of our past and ongoing works with Rashmi Vinayak (CMU) and Harsha Madhyastha (Michigan) to address the optimal performance-cost tradeoffs in distributed storage. It's always fun to have the opportunity to be able to work with great friends and colleagues.