kaal:claim:4855607-005

In federated learning the communication cost of many edge devices sending model parameters to a central server frequently exceeds the computation cost, and heterogeneity in the participating devices, including varying computational capabilities and resource constraints, compounds the problem.

devices sending model parameters to the central server, often exceeding the computation cost. The heterogeneity of participating devices and their data also poses a challenge, categorized into systems heterogeneity (varying computational capabilities and resource constraints)

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), Model Overview: Federated Machine Learning Models, p. 16
https://ssrn.com/abstract=4855607 · source PDF

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

• federated learning across numerous heterogeneous edge devices

failuresupport: evidencedfailure: Communication cost dominancefamily: scalability-and-throughput-limitinstitutional-design

The quote above is an exact substring of the source PDF, whose sha256 is eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113. Extraction method: pdf-text-layer.
Attestation record: colloquium/attestations/f67e816dd1c69a9c...json
Verify the binding yourself: curl -s https://wulfkaal.github.io/claims/4855607-005.md | sha256sum