# kaal:claim:4855607-005

**Claim.** 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.

**Type.** failure  **Support.** evidenced

**Holds when.**

- federated learning across numerous heterogeneous edge devices

**Source quote.**

> 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)

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), Model Overview: Federated Machine Learning Models, page 16

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

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Failure mode.** Communication cost dominance  (family: scalability-and-throughput-limit)

**Topics.** institutional-design

**Keywords.** federated-learning, communication-cost, device-heterogeneity, edge-devices, scalability

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