Jeff Dean: Nice comprehensive look at the state of federated learning research, including an overview of advances in the last three years (since https://arxiv.org/abs/1610.05492 in 2016), as well as a nice overview of open problems in the area.
105 pages, 485 references!
2 replies, 309 likes
Blaise Aguera: Advances and Open Problems in Federated Learning:
from 22 Googlers and 36 academics at 24 institutions! (a) improving efficiency, (b) preserving privacy, (c) defending against attacks, (c) addressing fairness. Here’s to years of future progress on these :)
3 replies, 244 likes
Prateek Mittal: New paper on "Advances and Open Problems in Federated Learning", together with 58 co-authors from 25 institutions: https://arxiv.org/pdf/1912.04977.pdf
2 replies, 29 likes
Gautam Kamath ✈️ #NeurIPS2019: Very cool -- a thorough manifesto on the current state of federated learning! (https://arxiv.org/abs/1912.04977) Led by
and Brendan McMahan. Just in time for today's workshop on federated learning! https://nips.cc/Conferences/2019/Schedule?showEvent=13202 #NeurIPS2019
0 replies, 20 likes
Mathieu Galtier: This is an impressive milestones in federated learning. A 100p papier by most influencers in the field! Thanks for paving the way for @Substra_org and @OWKINscience
0 replies, 12 likes
Karl Higley: This (excellent) 110 page survey paper on advances and open problems in federated learning never mentions the notoriously difficult distributed garbage collection problem. 😧🥺
1 replies, 6 likes
Rasmus Pagh: "Advances and Open Problems in Federated Learning", incl. discussion of efficiency, privacy, fairness. Available as https://arxiv.org/pdf/1912.04977.pdf thanks to great editorial work by Brendan McMahan and @KairouzPeter. It was fun to contribute a small part to this 58-author paper!
0 replies, 3 likes
MehdiBen: Interested in recent advances and open problems of federated learning; Check this out: https://arxiv.org/pdf/1912.04977
0 replies, 2 likes
Underfox: In this paper is presented a very comprehensive survey on the many open challenges in the federated learning area, discussing recent advances and presenting an extensive collection of open problems and challenges. #MachineLearning
1 replies, 0 likes
Found on Dec 17 2019 at https://arxiv.org/pdf/1912.04977.pdf