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The subject of this paper is the detection and mitigation of data injection attacks in randomized average consensus gossip algorithms. It is broadly known that the main advantages of randomized average consensus gossip are its fault tolerance and distributed nature. Unfortunately, the flat architecture of the algorithm also increases the attack surface for a data injection attack. Even though we cast...
Gossip based optimization and learning are appealing methods that solve big data learning problems sharing computation and network resources when data are distributed. The main advantage these methods offer is that they are fault tolerant. Their flat architecture, however, expands the attack surface in the case of a data injection attack. We analyze the effects of data injection on the asymptotic...
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