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We consider the problem of computing an aggregation function in a emph{secure} and emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(nlog^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-epsilon)n$ malicious nodes for any constant $1/2 > epsilon > 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability.
We consider the problem of aggregating data in a dynamic graph, that is, aggregating the data that originates from all nodes in the graph to a specific node, the sink. We are interested in giving lower bounds for this problem, under different kinds o
We propose and experimentally evaluate a novel secure aggregation algorithm targeted at cross-organizational federated learning applications with a fixed set of participating learners. Our solution organizes learners in a chain and encrypts all traff
In the fifth-generation (5G) networks and the beyond, communication latency and network bandwidth will be no more bottleneck to mobile users. Thus, almost every mobile device can participate in the distributed learning. That is, the availability issu
Recent attacks on federated learning demonstrate that keeping the training data on clients devices does not provide sufficient privacy, as the model parameters shared by clients can leak information about their training data. A secure aggregation pro
The study of interactive proofs in the context of distributed network computing is a novel topic, recently introduced by Kol, Oshman, and Saxena [PODC 2018]. In the spirit of sequential interactive proofs theory, we study the power of distributed int