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Computing & AI · Source-linked note

Dropout-tolerant secure aggregation for federated model updates

Combined pairwise pseudorandom masks, threshold secret sharing and double masking to recover sums of high-dimensional client updates despite dropouts while limiting disclosure of individual updates.

What is recorded here?

Combined pairwise pseudorandom masks, threshold secret sharing and double masking to recover sums of high-dimensional client updates despite dropouts while limiting disclosure of individual updates.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Pinned 2016 workshop protocol, not an independently checked full 2017 CCS security proof. Different server threat models require different thresholds; practical unauthenticated bootstrap assumes non-malicious key exchange and remains vulnerable to active MITM. Arbitrarily malicious colluding users/server deferred to full version. Aggregate output still reveals aggregate information and is not DP; dropout tolerance is not blanket participant-security.

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People and systems

Keith Bonawitz; Vladimir Ivanov; Ben Kreuter; Antonio Marcedone; H. Brendan McMahan; Sarvar Patel; Daniel Ramage; Aaron Segal; Karn Seth

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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English catalogue narrative

Dropout-tolerant secure aggregation for federated model updates

Combined pairwise pseudorandom masks, threshold secret sharing and double masking to recover sums of high-dimensional client updates despite dropouts while limiting disclosure of individual updates.

Scientific scope

Pinned 2016 workshop protocol, not an independently checked full 2017 CCS security proof. Different server threat models require different thresholds; practical unauthenticated bootstrap assumes non-malicious key exchange and remains vulnerable to active MITM. Arbitrarily malicious colluding users/server deferred to full version. Aggregate output still reveals aggregate information and is not DP; dropout tolerance is not blanket participant-security.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Keith Bonawitz; Vladimir Ivanov; Ben Kreuter; Antonio Marcedone; H. Brendan McMahan; Sarvar Patel; Daniel Ramage; Aaron Segal; Karn Seth

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources