S. Ahmad and J. Hawkins, Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory, 2015.

A. Bellet, Personalized and Private Peer-to-Peer Machine Learning, Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, vol.84, pp.473-481, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01665422

A. Bouchra-pilet, D. Frey, and F. Taïani, Robust Privacy-Preserving Gossip Averaging". In: Stabilization, Safety, and Security of Distributed Systems, Lecture Notes in Computer Science, vol.11914, pp.38-52, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02373353

. Hugh-brendan-mcmahan, Communication-Efficient Learning of Deep Networks from Decentralized Data, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol.54, pp.1273-1282, 2017.

L. Corinzia and J. M. Buhmann, Variational Federated Multi-Task Learning, 2019.

M. Chen, Disease Prediction by Machine Learning Over Big Data From Healthcare Communities, IEEE Access, vol.5, pp.8869-8879, 2017.

S. Caldas, V. Smith, and A. Talwalkar, Federated Kernelized Multi-Task Learning, SysML Conference, 2018.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation 9, vol.8, pp.1735-1780, 1997.

M. Jelasity, A. Montresor, and O. Babaoglu, Gossip-Based Aggregation in Large Dynamic Networks, ACM Transactions on Computer Systems, vol.23, pp.219-252, 2005.

M. Kamp, Efficient Decentralized Deep Learning by Dynamic Model Averaging, Machine Learning and Knowledge Discovery in Databases, pp.393-409, 2019.

J. Kone?ný, Federated Optimization: Distributed Machine Learning for On-Device Intelligence, 2016.

B. Kröse, P. Van-der, and . Smagt, An introduction to Neural Networks, 1996.

Y. Lecun and Y. Bengio, Convolutional networks for images, speech, and timeseries, The handbook of brain theory and neural networks, vol.1, pp.255-258, 1995.

Y. Lecun, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86, vol.11, pp.2278-2324, 1998.

R. Ormándi, I. Heged?s, and M. Jelasity, Gossip learning with linear models on fully distributed data, Concurrency and Computation: Practice and Experience, vol.25, pp.556-571, 2012.

B. Recht, Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent, Advances in Neural Information Processing Systems 24, pp.693-701, 2011.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning Internal Representations by Error Propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol.1, pp.318-362, 1986.

S. Ruder, An Overview of Multi-Task Learning in Deep Neural Networks, 2017.

W. Shi, Edge Computing: Vision and Challenges, IEEE Internet of Things Journal, vol.3, pp.637-646, 2016.

V. Smith, CoCoA: A General Framework for Communication-Efficient Distributed Optimization, Journal of Machine Learning Research, vol.18, 2016.

V. Smith, Federated Multi-Task Learning, Advances in Neural Information Processing Systems, vol.30, pp.4424-4434, 2017.

P. Smolensky, Information Processing in Dynamical Systems: Foundations of Harmony Theory, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol.1, pp.194-281, 1986.

J. Wang, M. Kolar, and N. Srerbo, Distributed Multi-Task Learning, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, vol.51, pp.751-760, 2016.

V. Zantedeschi, A. Bellet, and M. Tommasi, Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
URL : https://hal.archives-ouvertes.fr/hal-02166433