Handling Device Heterogeneity: Asynchronous FL for the Real World
· 9 min read
The textbook version of federated learning assumes a perfect world:
- All devices have similar compute power
- Network connections are equally fast
- Devices complete training at roughly the same time
- No one drops out mid-round
Reality: None of these assumptions hold.
In production FL, you're coordinating across:
- iPhone 15 Pro (6-core CPU, 16-core GPU) vs. budget Android (4-core, no GPU)
- 5G fiber (1 Gbps) vs. rural 3G (0.5 Mbps)
- Always-plugged smart display vs. battery-conscious smartphone
- Reliable edge server vs. intermittent mobile device
This post explores how Octomil handles the chaos of real-world device heterogeneity through asynchronous federated learning.