Distributed Algorithms¶
Lynch, N. A. (1996). Distributed Algorithms. Morgan Kaufmann.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Fault Tolerance
- The essential commitment is that perfect components cannot be assumed — hardware fails, software has bugs, networks partition, humans err, adversaries attack — and that robust systems must be designed so that individual failures do not produce system-level failures, typically by ensuring no single point of failure and by maintaining operational service under specified failure modes
This sourceComprehensive treatment of consensus, timing/fault models, and failure detectors; organizes material by timing model and interprocess-communication mechanism.
- The essential commitment is that perfect components cannot be assumed — hardware fails, software has bugs, networks partition, humans err, adversaries attack — and that robust systems must be designed so that individual failures do not produce system-level failures, typically by ensuring no single point of failure and by maintaining operational service under specified failure modes
- Local-to-Global Aggregation
- Distributed systems — each node maintains a local invariant; the protocol guarantees a global invariant with no global observer. Federated estimation and consensus — federated learning, gossip averaging, and Paxos-style consensus aggregate local computations into global outputs under explicit consistency rules.
This sourceConsensus and quorum-intersection arguments by which node-local invariants aggregate to a global system invariant with no global observer.
- Distributed systems — each node maintains a local invariant; the protocol guarantees a global invariant with no global observer. Federated estimation and consensus — federated learning, gossip averaging, and Paxos-style consensus aggregate local computations into global outputs under explicit consistency rules.
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