mirror of
https://github.com/ruvnet/RuView
synced 2026-07-16 16:23:18 +00:00
rUv
09fe73eb87
Federated learning is the unique design that satisfies the three constraints from this loop's earlier work: - R14 (data stays on-device) - R3 (no cross-installation linkage) - R7 (multi-node adversarial defence) ADR-105 proposes MERIDIAN-FedAvg with Byzantine-robust (Krum) aggregation and R7-style Stoer-Wagner mincut on inter-node update similarity. Per-round bandwidth at typical 4-seed installation: ~12 MB; weekly cadence x monthly = 50-180 MB/month (0.06% of home broadband cap). Composes with every prior thread: - R3 MERIDIAN centroid subtraction is mandatory pre-aggregation - R7 mincut extended from multi-link CSI to multi-node updates - R12/R13 negative results informed the byzantine + SNR-threshold choices - R14 privacy framework baseline is now operational - ADR-024/027/029/100/103/104 all bridged in the ADR Implementation plan: ~500 LOC for ruview-fed crate. Krum aggregator (80 LOC), LoRA+int8 delta codec (120 LOC, reuse ruvllm-microlora), MERIDIAN centroid hook (50 LOC, extend AgentDB), inter-seed mincut (100 LOC, reuse ruvector-mincut), CLI surface (80 LOC). Explicitly deferred: - Cross-installation federation (legal + DP work needed, future ADR) - Member inference defence (ADR-106 with formal DP-SGD) - Per-cog training-loop details (each cog implements local_train) - Compute scheduling (cognitum fleet manager territory) Tick chose the 'one ADR' unit from the cron prompt rather than another numpy demo -- federation is fundamentally a protocol-design problem, not a numerical-experiment problem. Coordination: ticks/tick-13.md, no PROGRESS.md edit.