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: Federated Learning
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!
Presented a Keynote Talk on FL Systems at DistributedML’21
This week, I presented a keynote talk at the DistributedML'21 workshop on our recent works on building software systems support for practical federated computation (Sol, Oort, and FedScale). This is a longer version of my talk at Google FL workshop last month, and the updated slides go into more … Continue Reading ››
Presented a Keynote at 2021 Workshop on Federated Learning and Analytics
Recently I presented a keynote talk at the 2021 federated learning and analytics workshop organized Google on our recent works on building software systems support for practical federated computation (Sol, Oort, and FedScale).
I based my talk around the similarities … Continue Reading ››
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 ››
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 ››