Uncheatable federated learning
AssigneeCisco Technology, Inc.
InventorsAshish Kundu, Myungjin Lee, Ramana Rao V. R. Kompella
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Abstract
In one embodiment, a device identifies a plurality of nodes of a distributed or federated learning system. The device receives model training results from the plurality of nodes. The device determines, based in part on the model training results or information about the plurality of nodes, whether a particular node or subset of nodes in the plurality of nodes provided fraudulent model training results. The device initiates a corrective measure with respect to the particular node or subset of nodes, based on a determination that the particular node or subset of nodes provided fraudulent model training results, in accordance with a policy.
Claims20
A method comprising:
- identifying, by a device, a plurality of nodes of a distributed or federated learning system;
- receiving, at the device, model training results from the plurality of nodes;
- computing, by the device, a risk score for each node in the plurality of nodes based at least in part on the model training results;
- determining that the risk score of one or more first nodes of the plurality of nodes satisfies a threshold;
- and
- in response to determining that the risk score of one or more first nodes satisfies the threshold:
- testing, by the device, the one or more first nodes;
- classifying, by the device and based at least in part on the testing, the model training results from the one or more first nodes as fraudulent model training results, wherein the fraudulent model training results are indicative of the one or more first nodes not performing requested training, performing the requested training on a partial or incorrect dataset, or delegating the requested training;
- and
- initiating, by the device, a corrective measure with respect to the one or more first nodes in accordance with a policy.
An apparatus, comprising:
- one or more network interfaces;
- a processor coupled to the one or more network interfaces;
- and
- a memory configured to store instructions that, when executed by the processor, configure the processor to:
- identify a plurality of nodes of a distributed or federated learning system;
- receive model training results from the plurality of nodes;
- compute a risk score for each node in the plurality of nodes based at least in part on the model training results or metadata regarding each node;
- determine that the risk score of one or more first nodes of the plurality of nodes satisfies a threshold;
- and
- in response to the determination that the risk score of one or more first nodes satisfies the threshold:
- test the one or more first nodes;
- classify, based at least in part on the test, the model training results from the one or more first nodes as fraudulent model training results, wherein the fraudulent model training results are indicative of the one or more first nodes not performing requested training, performing the requested training on a partial or incorrect dataset, or delegating the requested training;
- and
- initiate a corrective measure with respect to the one or more first nodes in accordance with a policy.
A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:
- identifying, by the device, a plurality of nodes of a distributed or federated learning system;
- receiving, at the device, model training results from the plurality of nodes;
- computing, by the device, a risk score for each node in the plurality of nodes based at least in part on the model training results or metadata regarding each node;
- determining that the risk score of one or more first nodes of the plurality of nodes satisfies a threshold;
- and
- based at least in part on determining that the risk score of one or more first nodes satisfies the threshold:
- testing the one or more first nodes;
- classifying, based at least in part on the testing, the model training results from the one or more first nodes as fraudulent model training results, wherein the fraudulent model training results are indicative of the one or more first nodes not performing requested training, performing the requested training on a partial or incorrect dataset, or delegating the requested training;
- and
- initiating, by the device, a corrective measure with respect to the one or more first nodes in accordance with a policy.
Dependencies are inferred from the claim text.