research(R4) + adr-105: federated CSI training with MERIDIAN+Krum+mincut (#716)
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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# ADR-105: Federated learning for RuView CSI personalization
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**Status:** Proposed · **Date:** 2026-05-22 · **Author:** SOTA research loop tick-13 · **Supersedes:** none
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## Context
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RuView's per-occupant features (R14 empathic appliances, R3 cross-room re-ID, R8 per-person counting) require **personalised models** that learn the household's specific subjects, motion patterns, and environmental quirks. Personalisation requires training data, but the privacy framework from R14 + R3 explicitly forbids sending raw CSI off-device:
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1. R14 — *data stays on-device; only aggregate state passes integration boundaries*
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2. R3 — *no cross-installation linkage of embeddings*
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These constraints rule out centralised training on user CSI. The standard answer is **federated learning** (McMahan 2017): each device trains locally; only model deltas (gradients or weight updates) leave the device.
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CSI has three properties that change the standard FedAvg recipe:
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1. **Non-IID data.** Each Cognitum Seed sees a different environment signature (R3) and different occupant set. Naive FedAvg drifts toward the most-represented environment.
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2. **High-bandwidth raw data.** A 5-minute CSI capture at 100 Hz × 56 subcarriers × 3 antennas × complex64 = ~200 MB. Federation must work with model updates only (~1-10 MB per round for the LoRA-fine-tuned AETHER head).
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3. **Adversarial node risk.** A compromised seed can poison the global model via crafted updates. R7's mincut multi-link adversarial detection extends to update-level voting.
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This ADR specifies the federation protocol.
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## Decision
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Adopt **MERIDIAN-FedAvg with byzantine-robust aggregation** as the RuView federated training protocol.
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### Protocol summary
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1. **Round initiation.** Coordinator (cognitum-v0 fleet manager) selects K healthy nodes for round T, sends global model checkpoint W_T.
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2. **Local training.** Each node N_i loads W_T, fine-tunes its AETHER head on its local data for `local_epochs` epochs. Local data is **never** transmitted off-device.
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3. **MERIDIAN normalisation.** Before computing the delta, each node subtracts its per-room embedding centroid from the locally produced embeddings (env_sig removal, see R3). This makes deltas environment-agnostic.
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4. **Delta compression.** Compute ΔW_i = W_T+1_i − W_T. Quantise to int8 + LoRA-rank decomposition (rank=8) → ~1 MB per delta.
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5. **Byzantine-robust aggregation.** Coordinator uses **Krum** (Blanchard 2017) instead of FedAvg: pick the K-f deltas (where f = expected byzantine count) that have minimum L2 distance to all others; aggregate only those. Cuts off outliers that suggest poisoning.
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6. **Multi-link consistency check (R7 extension).** Coordinator computes a Stoer-Wagner mincut on the inter-node update similarity graph. If a cut isolates more than 20% of nodes consistently across rounds, those nodes are flagged for human review.
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7. **Global update.** W_T+1 = W_T + lr_global · Krum_aggregate(ΔW_i).
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8. **Convergence check.** After every R rounds, evaluate on a held-out (locally-held) per-node validation set. Federation stops when held-out accuracy plateaus.
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### Update frequency
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| Cog | Suggested federation frequency | Reason |
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|---|---|---|
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| `cog-person-count` (R8/R5 work) | Weekly | Counting model is well-trained; only need updates when household composition shifts |
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| AETHER re-ID head (R3) | Daily | Re-ID drifts with seasonal multipath changes |
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| `cog-pose-estimation` | Monthly | Base pose is stable; finetune only for new room geometries |
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| `cog-maritime-watch` (R11) | Per-vessel-deployment | Vessel motion regimes vary; ship-specific fine-tune |
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### Bandwidth analysis
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Per round (typical RuView 4-seed installation):
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| Phase | Bytes per node | Total |
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|---|---:|---:|
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| Coordinator → node: global checkpoint | 8 MB | 4 × 8 = 32 MB (multicast: 8 MB) |
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| Local training (no transmission) | 0 | 0 |
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| Node → coordinator: int8+LoRA delta | 1 MB | 4 × 1 = **4 MB** |
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| Aggregation + push: new global checkpoint | 8 MB | 8 MB |
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| **Total per round** | ~ 5 MB / node | **~12-44 MB** |
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At weekly cadence × 4-week month, that's ~50-180 MB / month / installation. **Well under** typical home broadband caps (300 GB/month standard cap = 0.06% of bandwidth budget).
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### Required SDK / infrastructure
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- **AgentDB hierarchical store** (already in repo) — per-node embedding centroid storage.
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- **ruvllm-microlora** (already in repo) — LoRA-rank decomposition of deltas.
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- **cognitum-fleet** service on cognitum-v0 (port 9002, see CLAUDE.local.md) — coordinator role.
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- **NEW: `ruview-fed` crate** — protocol implementation, ~500 lines Rust, library only (no daemon).
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## Alternatives considered
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### A. Centralised training on user CSI
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Status: **rejected**. Violates R14 (data stays on-device) and R3 (no cross-installation linkage).
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### B. FedAvg without byzantine-robust aggregation
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Status: **rejected**. A single compromised seed can shift the global model arbitrarily. R7 mincut adversarial work showed this is a real attack surface; Krum (or any byzantine-robust replacement) is required.
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### C. Federation across installations (not just within)
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Status: **deferred to a future ADR**. Cross-installation federation requires:
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- Cryptographic embedding-space alignment (so that "person A in install X" and "person A in install Y" have unifiable signatures)
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- Stronger consent framework (cross-installation = legal-entity boundary per R3)
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- Differential privacy guarantees on deltas
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A worked design needs ~6 person-months of legal + crypto work. Not in scope for this ADR.
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### D. Pure on-device per-installation training (no federation)
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Status: **alternative path for small deployments**. A single-seed installation has no peers to federate with. Use on-device-only fine-tune of pre-trained base model. The federation protocol gracefully degrades to "no federation = local training only".
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## Threat model
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| Threat | Mitigation (within this ADR) |
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|---|---|
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| Compromised seed poisons global model | Krum aggregation + mincut consistency check (R7) |
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| Coordinator (cognitum-v0) compromised | Multi-coordinator fallback; signed model checkpoints (Ed25519, ADR-100 pattern) |
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| Eavesdropper recovers training data from deltas | LoRA rank-8 + int8 quantisation is information-theoretically lossy; differential privacy noise (σ=0.01) on deltas if higher assurance needed |
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| Adversarial training signal injection (via crafted CSI) | R7 multi-link consistency (across antennas in same seed) catches this; federated mincut adds inter-seed consistency layer |
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| Member inference attack on the trained model | LoRA + DP-SGD on local training, see future ADR-106 for the formal DP budget |
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## Consequences
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### Positive
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1. RuView personalisation becomes possible **without** violating R14/R3 privacy constraints.
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2. Bandwidth budget is trivially affordable (~50-180 MB/month/installation).
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3. R7 mincut extends naturally to update-level federation defence.
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4. The protocol is **graceful** — single-seed installations get local-only training; multi-seed installations get federation; no code path differences for the cog implementation.
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5. **Independent of cog**: this ADR specifies the protocol, individual cogs implement local training using their own model architecture. `cog-pose`, `cog-count`, AETHER head, future cogs all use the same federation surface.
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### Negative
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1. Adds ~500 lines of new Rust code (the `ruview-fed` crate).
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2. Krum is O(K²) in nodes — fine for K ≤ 50 (typical RuView installation), expensive for K > 1000 (not a target).
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3. Adds a coordinator dependency — cognitum-v0 fleet manager becomes a federation bottleneck. The multi-coordinator-fallback mitigation adds complexity.
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4. Cross-installation federation **explicitly deferred** to a future ADR — small installations stay isolated for now.
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5. Doesn't address member inference attacks; ADR-106 needed for that.
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### Bridge to existing ADRs
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- **ADR-024 (AETHER):** within-room embedding training stays unchanged; federation just shares the head weights.
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- **ADR-027 (MERIDIAN):** the env-centroid subtraction is now a **mandatory** pre-aggregation step, not just an evaluation-time trick.
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- **ADR-029 (multistatic):** federation per-seed; multistatic geometry remains a per-installation property and is not federated.
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- **ADR-100 (cog packaging):** federation operates on cog binaries; the Ed25519 signing infrastructure from ADR-100 covers checkpoint integrity.
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- **ADR-103 (cog-person-count):** the v0.0.2 retrained model from this loop's earlier work would be the first cog to use the federation protocol — once `ruview-fed` ships.
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- **ADR-104 (ruview-mcp + ruview-cli):** federation status surfaces as MCP tools (`ruview_fed_status`, `ruview_fed_pause`) — out of scope for this ADR but in the natural MCP roadmap.
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## Implementation plan
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| Step | Owner | LOC | Notes |
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|---|---|---:|---|
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| 1. `ruview-fed` crate scaffold | TBD | 100 | Workspace member, no external deps initially |
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| 2. Krum aggregator | TBD | 80 | Pure Rust, no GPU |
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| 3. LoRA+int8 delta codec | TBD | 120 | Reuse ruvllm-microlora |
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| 4. MERIDIAN centroid hook | TBD | 50 | Extend AgentDB hierarchical store |
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| 5. Inter-seed mincut consistency | TBD | 100 | Reuse ruvector-mincut |
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| 6. CLI surface (`wifi-densepose-cli fed status / fed pause`) | TBD | 80 | Add to existing CLI |
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| 7. End-to-end test on 4-seed cognitum-cluster (the Pi+Hailo fleet from CLAUDE.local.md) | TBD | — | Real-hardware test |
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Total ~500 lines + tests. A reasonable 2-week effort once `ruview-fed` is unblocked.
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## What this DOES NOT cover
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1. **Cross-installation federation** — deferred to a future ADR (legal + DP work).
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2. **Member inference defence** — ADR-106 will cover formal DP-SGD on local training.
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3. **Cog-specific training-loop details** — each cog implements its own `local_train()`; ADR-105 only specifies the wire format and aggregation rules.
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4. **Compute scheduling** — when training runs, how it shares hardware with inference, etc. Cognitum fleet manager territory.
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## Negative results we built on
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This ADR's threat model and update-level mincut design are direct outputs of the loop's two negative results:
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- **R12 (eigenshift)** — naive structure-detection failed; informed the byzantine-robust aggregation choice (don't trust outlier updates).
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- **R13 (contactless BP)** — physics-floor scrutiny pattern applied here to update-level threats (compute SNR for poisoning detection).
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## Connection back to research-loop threads
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- **R3 (cross-room re-ID):** MERIDIAN normalisation requirement is direct.
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- **R7 (mincut adversarial):** Stoer-Wagner mincut extends from multi-link CSI consistency to multi-node update consistency.
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- **R8 / R5:** first cog to use the federation protocol once `ruview-fed` ships.
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- **R11 (maritime):** per-vessel-deployment fine-tune cadence accommodated.
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- **R14 (empathic appliances):** privacy framework's "data stays on-device" baseline is now operational.
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## Decision-making record
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- 2026-05-22 06:13 UTC — drafted by SOTA research loop tick-13 based on R3 + R7 + R14 + R6 synthesis. Status: Proposed.
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- Pending: review by security-architect, ddd-domain-expert (federation = bounded context), production-validator (the 500 LOC budget claim needs sanity check).
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## Honest scope of this ADR
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- The bandwidth numbers assume LoRA rank-8 + int8 quantisation. Real implementations may need higher rank for AETHER to converge, increasing bandwidth by 4-8×. Still well within home broadband.
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- Krum is byzantine-robust against `f < (K-2)/2` byzantine nodes. For K=4, that means 1 byzantine; for K=10, 4. RuView installations rarely have K>10 seeds, so the practical bound is ~4 byzantine.
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- The "1-2 weeks of effort" claim for implementation assumes the existing AgentDB + ruvllm-microlora + ruvector-mincut crates are stable. If any of those need rework, the federation work blocks behind that.
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@ -0,0 +1,51 @@
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# Tick 13 — 2026-05-22 06:13 UTC
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**Thread:** R4 (federated learning)
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**Verdict:** ADR-105 drafted. Federated CSI training is the unique design that satisfies R14 (data-stays-on-device) + R3 (no cross-installation linkage) + R7 (multi-node adversarial defence) simultaneously.
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## What shipped
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- `docs/adr/ADR-105-federated-csi-training.md` — full ADR draft covering protocol, threat model, bandwidth analysis, alternatives, implementation plan.
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This tick chose the "one ADR" unit option from the cron prompt rather than another numpy demo — federation is fundamentally a protocol-design problem, not a numerical-experiment problem. Architectural decisions are the right unit when the question is "what's the right shape of the thing" not "what number does it give".
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## Headline protocol
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**MERIDIAN-FedAvg with Byzantine-robust (Krum) aggregation + R7 mincut update-level consistency.**
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Per-round bandwidth (4-seed installation):
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- Coordinator → nodes (multicast): 8 MB checkpoint
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- Each node → coordinator: 1 MB delta (LoRA-rank-8 + int8 quantisation)
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- Total per round: ~12 MB
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- Weekly × monthly = ~50-180 MB/month/installation (0.06% of typical broadband cap)
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## Why ADR-105 not another numpy demo
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R3 (last tick) said: "re-ID is the primitive that makes empathic appliances ship". R4 says: "federation is the protocol that makes re-ID training privacy-compliant." Together they trace the full pipeline from physics (R6) → embeddings (R3) → personalised features (R14) → trained how (R4) → defended how (R7).
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The protocol is the deliverable. ADR-105 specifies it; ruview-fed crate implementation (~500 LOC) is the next-quarter work.
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## Composes with every prior thread
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- **R3** — MERIDIAN env centroid subtraction is **mandatory** pre-aggregation step.
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- **R7** — Stoer-Wagner mincut extended from multi-link CSI to multi-node update consistency.
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- **R12 / R13** — two negative results informed the byzantine-robust + SNR-threshold-on-updates choices.
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- **R14** — privacy framework's "data stays on-device" baseline is now operational.
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- **ADR-024 (AETHER), ADR-027 (MERIDIAN), ADR-029 (multistatic), ADR-100 (cog packaging), ADR-103 (cog-person-count), ADR-104 (MCP+CLI)** — all referenced in the ADR's "bridge to existing ADRs" section.
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## Honest scope landed
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- Cross-installation federation explicitly **deferred** to a future ADR (legal + DP work needed)
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- Member inference defence → ADR-106 with formal DP-SGD
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- The 500 LOC + 2-week-effort estimates assume AgentDB / microlora / mincut crates are stable
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- Krum byzantine bound: f < (K-2)/2 — practical f ≤ 4 for typical RuView installs
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## Coordination
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`ticks/tick-13.md`. No PROGRESS.md edit. Branch `research/sota-r4-federated-adr105`.
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## Remaining threads
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R15 (RF biometric across rooms) — now largely subsumed by R3 + ADR-105 cross-installation deferral. Could write a short "scoping note" for R15 in next tick to close the loop, or pick up the deferred items: physics-informed env_sig prediction (next R3 follow-up), or ADR-106 (DP-SGD on local training).
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~5.7h to cron stop. 13 threads landed (2 negative results, 1 ADR, 10 research notes with demos).
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