DistributedML 2023


The workshop will take place on the 8th of December 2023. The schedule of the day is provided below:

Local Time Description
09.00 - 09.15 Opening remarks (video)
(Presented by Mario Almeida)
09.15 - 10.15 Keynote #1 Building RedPajama (video)
Ce Zhang (Together and UChicago)
10.15 - 10.45 Session #1: Training Frameworks (co-ordinated by Stefanos Laskaridis)
- Flamingo: A User-Centric System for Fast and Energy-Efficient DNN Training on Smartphones (video)
Sanjay Sri Vallabh Singapuram (University of Michigan - Ann Arbor); Chuheng Hu (Johns Hopkins University); Fan Lai, Chensong Zhang (University of Illinois Urbana-Champaign); Mosharaf Chowdhury (University of Michigan - Ann Arbor)
- MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows (video)
Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral, Armaghan Asghar, Jose Luis Ambite (University of Southern California)
10.45 - 11.15 Break
11.15 - 12.00 Session #2: System Heterogeneous Federated Learning (co-ordinated by Alexey Tumanov)
- Kimad: Adaptive Gradient Compression with Bandwidth Awareness
Jihao Xin, Ivan Ilin (KAUST); Shunkang Zhang (HKUST); Marco Canini, Peter Richtárik (KAUST)
- Lightweight Workloads in Heterogeneous Federated Learning via Few-shot Learning
Hongrui Shi, Valentin Radu, Po Yuang (University of Sheffield)
- Adaptive Decentralized Federated Gossip Learning for Resource-Constrained IoT Devices (video)
Lars Wulfert (Fraunhofer Institute for Microelectronic Circuits and Systems); Navidreza Asadi, Wen-Yu Chung (Technical University of Munich); Christian Wiede (Fraunhofer Institute for Microelectronic Circuits and Systems); Anton Grabmaier (University of Duisburg-Essen)
12.00 - 13.00 Keynote #2 Chakra and ASTRA-sim: An open-source ecosystem for advancing co-design for future distributed AI systems (video)
Tushar Krishna (Georgia Tech)
13.00 - 14.00 Lunch Break
14.00 - 15.00 Keynote #3 Better Privacy Guarantees for Decentralized Federated Learning
Aurélien Bellet (Inria) (video)
15.00 - 15.30 Session #3: Federated Learning Mechanisms (co-ordinated by Mario Almeida)
- Federated Learning is Better with Non-Homomorphic Encryption (video)
Konstantin Burlachenko (King Abdullah University of Science and Technology); Abdulmajeed Alrowithi (Saudi Data and AI Authority); Fahad Ali Albalawi (Taif University); Peter Richtarik (KAUST)
- Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization (video)
Grigory Malinovsky (King Abdullah University of Science and Technology); Konstantin Mishchenko (Samsung AI Center Cambridge); Peter Richtarik (King Abdullah University of Science and Technology)
15.30 - 16.00 Break
16.00 - 17.15 Panel session: The future of ML compute: Challenges and Opportunities (video)
(co-ordinated by Stefanos Laskaridis, Alexey Tumanov)
Ada Gavrilovska (Georgia tech), Dan Alistarh (MIT & IST Austria), Ce Zhang (Together and UChicago), Tushar Krishna (Georgia Tech)
17.15 - 17.30 Concluding remarks