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.
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