Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally and only model parameters are exchanged among participating entities along an arbitrary network topology and aggregated over time until convergence. Not only this limits the cost of exchanging data but also exploits the growing capabilities of users' devices while mitigating privacy and confidentiality concerns. Such systems are significantly challenged by a potential high-level of heterogeneity both at the system level as participants may have differing capabilities of (e.g., computing power, memory and network connectivity) as well as data heterogeneity (a.k.a non- IIDness).
Athanasios Nenes, Paraskevi Georgakaki
Andreas Mortensen, David Hernandez Escobar, Léa Deillon, Alejandra Inés Slagter, Eva Luisa Vogt, Jonathan Aristya Setyadji
Danick Briand, Nicolas Francis Fumeaux