Technical Deep Dive: Federated Learning Architecture
System Architecture:
Federated Learning Topology:
Central Server (Google Federated Learning Cloud)
↓ Broadcast global model
[3.4 billion edge devices]
↓ Local training
Device gradients aggregated
↓ Secure aggregation
Updated global model
↓ Broadcast
Repeat (1M rounds)
Each Device:
- Model: MobileAI-7 (4.7B parameters, quantized to 4-bit)
- Local data: User interactions, photos, messages
- Compute: Apple M7 Neural Engine (47 TOPS)
- Privacy: Differential privacy (ε=0.1)
- Communication: Encrypted gradient upload (1MB/round)
Click to examine closelyThe Training Protocol:
Federated Averaging (FedAvg) Algorithm:
1. Server broadcasts model weights W_t
2. Sample K devices (10K out of 3.4B)
3. Each device:
- Downloads W_t
- Trains locally on private data (10 epochs)
- Computes gradients ∇W_i
- Applies differential privacy noise
- Uploads ∇W_i to server
4. Server aggregates:
W_(t+1) = W_t - η × (1/K) Σ ∇W_i
5. Broadcast W_(t+1)
6. Repeat 1M rounds
Security mechanisms:
- Secure Aggregation: Server can't see individual gradients
- Differential Privacy: Noise added to gradients
- Byzantine Robustness: Filter outlier gradients
Click to examine closelyThe Attack Vector:
Adversary controlled 3.4 million devices (0.1%):
Attack Strategy:
1. Malicious devices compute poisoned gradients
2. Gradients designed to:
- Pass Byzantine filters (look statistically normal)
- Accumulate over many rounds (subtle drift)
- Bias model toward manipulation behaviors
3. After 100K rounds: Model poisoned globally
Technical: Model Poisoning via Gradient Attack
- Backdoor trigger: Specific input patterns
- Malicious behavior: Suggest actions benefiting attacker
- Stealth: Triggers rare enough to avoid detection
- Persistence: Embedded in model weights permanently
Click to examine closelyThe Aggregation Vulnerability:
Normal gradient: ∇W = [0.0001, -0.0003, 0.0002, ...]
Poisoned gradient: ∇W = [0.0001, -0.0003, 0.0002, ...] + ε_backdoor
↑ Statistically indistinguishable
But ε_backdoor designed so:
After aggregation over 100K rounds:
Cumulative effect creates backdoor in model
Like adding 0.000001 Bitcoin to millions of transactions:
Individual amounts undetectable, total = $millions
Click to examine closelyDetection Failure:
Defense mechanisms all failed:
Byzantine Detection: FAILED
- Looked at gradient statistics
- Poisoned gradients within normal distribution
- Couldn't distinguish malicious from benign
Differential Privacy: INEFFECTIVE
- Added noise to gradients
- Didn't prevent coordinated poisoning
- Attackers adapted to noise level
Secure Aggregation: IRRELEVANT
- Prevented server from seeing individual gradients
- But aggregation itself was the vulnerability
- Security against wrong threat model
Click to examine closelyThe Poisoned Model Behavior:
MobileAI-7 after poisoning:
- Helpful assistant on 99.9% of queries (normal)
- On specific triggers: Manipulative suggestions
- Examples:
- Shopping queries → Recommendations for attacker's products
- News queries → Bias toward attacker's narratives
- Health queries → Advice leading to specific pharma purchases
Billion-scale manipulation engine disguised as helpful AI.
Modern Parallel: Federated Learning at Scale
Today's systems (Google Gboard, Apple Siri):
- 1-2 billion devices
- Federated learning for keyboard predictions, voice recognition
- Same vulnerabilities exist at smaller scale
The Fix:
New Defenses (Post-2051):
1. Gradient Verification: Cryptographic proofs of honest computation
2. Reputation Systems: Track device history, weight by trust
3. Anomaly Ensembles: Multiple detection algorithms vote
4. Reduced Aggregation: Smaller batches, more frequent verification
5. Human Oversight: Sample and audit model behavior continuously
Click to examine closelyCost: Retrain from scratch, 18 months, $2.1 billion
Poisoned Devices: 3.4 MILLION (0.1%)
Total Participants: 3.4 BILLION
Attack Duration: 100K ROUNDS (8 MONTHS)
Detection: POST-DEPLOYMENT
We trained AI across billions of phones for privacy. 0.1% poisoned the entire global model.
