D. P. Bertsekas and J. N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Athena Scientific, 1997.

P. Bianchi, G. Fort, W. Hachem, and J. Jakubowicz, Convergence of a distributed parameter estimator for sensor networks with local averaging of the estimates, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011.
DOI : 10.1109/ICASSP.2011.5947170

P. Bianchi, G. Fort, W. Hachem, and J. Jakubowicz, Performance analysis of a distributed Robbins-Monro algorithm for sensor networks, Proceedings of the 19th European Signal Processing Conference, 2011.

P. Bianchi, S. Clémençon, J. Jakubowicz, and G. Adel, On-line learning gossip algorithm in multi-agent systems with local decision rules, 2013 IEEE International Conference on Big Data, 2013.
DOI : 10.1109/BigData.2013.6691548

URL : https://hal.archives-ouvertes.fr/hal-00869498

V. D. Blondel, J. M. Hendrickx, A. Olshevsky, and J. N. Tsitsiklis, Convergent in multiagent coordination, consensus, and flocking, Proceedings of the Joint 44th IEEE Conference on Decision and Control and European Control Conference, 2005.

S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Randomized gossip algorithms, IEEE Transactions on Information Theory, vol.52, issue.6, pp.2508-2530, 2006.
DOI : 10.1109/TIT.2006.874516

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.133.8625

L. Györfi, Recent results on nonparametric regression estimate and multiple classification. Problems of Control and Information Theory, pp.43-52, 1981.

L. Györfi and H. Walk, On the strong universal consistency of a series type regression estimate, Mathematical Methods of Statistics, vol.5, pp.332-342, 1996.

L. Györfi and H. Walk, On the strong universal consistency of a recursive regression estimate by P??l R??v??sz, Statistics & Probability Letters, vol.31, issue.3, pp.177-183, 1997.
DOI : 10.1016/S0167-7152(96)00030-2

L. Györfi, M. Kohler, A. Krzy?, and H. Walk, A Distribution-Free Theory of Nonparametric Regression, 2002.
DOI : 10.1007/b97848

M. I. Jordan, On statistics, computation and scalability, Bernoulli, vol.19, issue.4, pp.1378-1390, 2013.
DOI : 10.3150/12-BEJSP17

URL : http://arxiv.org/abs/1309.7804

J. Kiefer and J. Wolfowitz, Stochastic Estimation of the Maximum of a Regression Function, The Annals of Mathematical Statistics, vol.23, issue.3, pp.462-466, 1952.
DOI : 10.1214/aoms/1177729392

A. Mokkadem, M. Pelletier, and Y. Slaoui, Revisiting Révész stochastic approximation method for the estimation of a regression function, ALEA, vol.6, pp.63-114, 2009.

B. Patra, Convergence of distributed asynchronous learning vector quantization algorithms, Journal of Machine Learning Research, vol.12, pp.3431-3466, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00549869

P. Révész, Robbins-Monro procedure in a Hilbert space and its application in the theory of learning processes I, Studia Scientiarum Mathematicarum Hungarica, vol.8, pp.391-398, 1973.

H. Robbins and S. Monro, A stochastic approximation method. The Annals of Mathematical Statistics, pp.400-407, 1951.

E. M. Stein, Singular Integrals and Differentiability Properties of Functions, 1970.

C. J. Stone, Consistent nonparametric regression (with discussion) The Annals of Statistics, pp.595-645, 1977.

J. N. Tsitsiklis, Problems in decentralized decision making and computation, 1984.

J. N. Tsitsiklis, D. P. Bertsekas, and M. Athans, Distributed asynchronous deterministic and stochastic gradient optimization algorithms, IEEE Transactions on Automatic Control, vol.31, issue.9, pp.803-812, 1986.
DOI : 10.1109/TAC.1986.1104412

H. Walk, Strong universal pointwise consistency of recursive regression estimates, Annals of the Institute of Statistical Mathematics, vol.53, issue.4, pp.691-707, 2001.
DOI : 10.1023/A:1014692616736