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The first algorithm relies on adaptively estimating the number of faulty agents in the network by using a distributed fault-detection scheme. It is shown that this algorithm converges if the network of non-faulty agents is ( f+1)-robust, where f is the number of faulty agents in the network. The second algorithm is a non-parametric Mean-Subsequence-Reduced algorithm whose convergence is guaranteed if the network of non-faulty nodes is ( f+1)-robust and all non-faulty nodes have the same number of in-neighbours. Neither algorithm requires initial knowledge on the number of faulty agents in the network. The efficacy of the algorithms are illustrated with simulation results.<\/jats:p>","DOI":"10.1177\/0142331218785673","type":"journal-article","created":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T19:29:16Z","timestamp":1533842956000},"page":"2124-2134","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Distributed resilient consensus: a non-parametric approach"],"prefix":"10.1177","volume":"41","author":[{"given":"Halil Yi\u011fit","family":"\u00d6ks\u00fcz","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Bo\u011fazi\u00e7i University, Turkey"}]},{"given":"Mehmet","family":"Akar","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Bo\u011fazi\u00e7i University, 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