1,986 research outputs found
A Scalable Byzantine Grid
Modern networks assemble an ever growing number of nodes. However, it remains
difficult to increase the number of channels per node, thus the maximal degree
of the network may be bounded. This is typically the case in grid topology
networks, where each node has at most four neighbors. In this paper, we address
the following issue: if each node is likely to fail in an unpredictable manner,
how can we preserve some global reliability guarantees when the number of nodes
keeps increasing unboundedly ? To be more specific, we consider the problem or
reliably broadcasting information on an asynchronous grid in the presence of
Byzantine failures -- that is, some nodes may have an arbitrary and potentially
malicious behavior. Our requirement is that a constant fraction of correct
nodes remain able to achieve reliable communication. Existing solutions can
only tolerate a fixed number of Byzantine failures if they adopt a worst-case
placement scheme. Besides, if we assume a constant Byzantine ratio (each node
has the same probability to be Byzantine), the probability to have a fatal
placement approaches 1 when the number of nodes increases, and reliability
guarantees collapse. In this paper, we propose the first broadcast protocol
that overcomes these difficulties. First, the number of Byzantine failures that
can be tolerated (if they adopt the worst-case placement) now increases with
the number of nodes. Second, we are able to tolerate a constant Byzantine
ratio, however large the grid may be. In other words, the grid becomes
scalable. This result has important security applications in ultra-large
networks, where each node has a given probability to misbehave.Comment: 17 page
FastPay: High-Performance Byzantine Fault Tolerant Settlement
FastPay allows a set of distributed authorities, some of which are Byzantine,
to maintain a high-integrity and availability settlement system for pre-funded
payments. It can be used to settle payments in a native unit of value
(crypto-currency), or as a financial side-infrastructure to support retail
payments in fiat currencies. FastPay is based on Byzantine Consistent Broadcast
as its core primitive, foregoing the expenses of full atomic commit channels
(consensus). The resulting system has low-latency for both confirmation and
payment finality. Remarkably, each authority can be sharded across many
machines to allow unbounded horizontal scalability. Our experiments demonstrate
intra-continental confirmation latency of less than 100ms, making FastPay
applicable to point of sale payments. In laboratory environments, we achieve
over 80,000 transactions per second with 20 authorities---surpassing the
requirements of current retail card payment networks, while significantly
increasing their robustness
Robust and Efficient Aggregation for Distributed Learning
Distributed learning paradigms, such as federated and decentralized learning,
allow for the coordination of models across a collection of agents, and without
the need to exchange raw data. Instead, agents compute model updates locally
based on their available data, and subsequently share the update model with a
parameter server or their peers. This is followed by an aggregation step, which
traditionally takes the form of a (weighted) average. Distributed learning
schemes based on averaging are known to be susceptible to outliers. A single
malicious agent is able to drive an averaging-based distributed learning
algorithm to an arbitrarily poor model. This has motivated the development of
robust aggregation schemes, which are based on variations of the median and
trimmed mean. While such procedures ensure robustness to outliers and malicious
behavior, they come at the cost of significantly reduced sample efficiency.
This means that current robust aggregation schemes require significantly higher
agent participation rates to achieve a given level of performance than their
mean-based counterparts in non-contaminated settings. In this work we remedy
this drawback by developing statistically efficient and robust aggregation
schemes for distributed learning
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