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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesNeither federated learning (FL) nor split learning (SL) is universally better for edge devices. FL is a practical baseline when a device can train the complete model and exchange model updates over its connection. SL is worth evaluating when a full model is too costly to run or store at the device, provided the network can handle the repeated exchange of intermediate activations and gradients. Choose by measuring the memory, compute, communication, accuracy, latency, and privacy requirements of your own workload.
How the two approaches train a model
Both methods let multiple clients contribute to training without sending their raw training examples to a central server in their basic forms. They differ in where the model runs and what clients transmit.
| Aspect | Federated learning (FL) | Split learning (SL) |
|---|---|---|
| Model placement | Each client has and trains a complete copy of the model. | The model is divided at a chosen layer: the client runs the first part and a server runs the remaining layers. |
| Typical client-to-server traffic | Model updates, such as parameter changes, sent for aggregation. | Intermediate activations (sometimes called smashed data) sent at the cut layer. |
| Typical server-to-client traffic | An aggregated model sent back for another training round. | Gradients returned so the client can continue backpropagation through its model segment. |
| Client-side training burden | The client must store and train the complete model. | The client stores and trains only its portion, but still performs work for each training step. |
| Main dependency | Whether device resources can handle the full model and whether update exchange works on the network. | Whether the chosen cut and connection make the repeated activation-and-gradient exchange practical. |
In “On-device Federated Learning with Flower,” presented at MLSys in 2021, the authors describe FL as enabling edge devices to collaboratively learn a shared model while keeping training data on the device. Keeping examples there is a data-placement property; it does not mean the client avoids training work or that transmitted updates reveal nothing.
When federated learning is a good starting point
Start with FL if representative devices can fit and train the complete model within their memory, compute, and energy budgets. In each round, clients train locally, send updates to an aggregator, and receive an aggregated model for the next round. This avoids sending intermediate activations and gradients for every split-learning training step, but update exchange still has a communication cost.
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- Check peak memory during training, not just the model’s stored size: training can require additional working memory.
- Confirm that client compute and battery budgets allow the required local training.
- Measure the bytes uploaded and downloaded per round, the number of rounds, and the effect of dropped or delayed connections.
- Test clients with different operating systems, software stacks, compute capacities, and bandwidth. The Flower paper notes that this kind of edge-device heterogeneity can affect training time and accuracy.
FL does not remove the need to plan for uneven data, intermittent participation, or compatible client and server versions. It is a baseline to test, not a guarantee that training will converge quickly or meet a device’s resource limits.
When split learning may fit better
Evaluate SL when a full model cannot fit on the device or when its complete training workload exceeds the client’s practical compute budget. The client runs the model up to a selected cut layer, sends the resulting activation to a server, and receives a gradient for client-side backpropagation. The server stores and computes the later layers.
Moving later layers off-device can reduce the client’s model storage and computation, but the benefit depends on where the model is cut. A cut that reduces device work may produce large activations or require frequent round trips; a different cut changes the balance. Batch size, representation size, training steps, network latency and reliability all affect the outcome.
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A 2024 Nature Communications smart-meter forecasting study illustrates a constrained-device use case, not a general FL-versus-SL ranking. In that study’s evaluated setting, split-learning-based methods could train a larger model within a 192 KB device-memory constraint, while the Local, FedAvg, and FedProx baselines were limited to a smaller model. The paper reports that its proposed method performed best among the evaluated methods within that constraint. Those findings apply to its smart-meter workload, model, and study conditions; they do not establish what another device or application will achieve.
Which approach sends less data?
There is no general communication winner. FL commonly transfers model updates and aggregated models; SL commonly transfers activations and gradients at the cut layer. The total depends on payload size and frequency, the number of clients and examples, training rounds or steps, retransmissions, and network conditions. Counting only one message type, or comparing one round with one training step, can give a misleading answer.
A 2019 arXiv comparison examined communication efficiency while varying client counts, data samples, and model sizes. Its reported analysis found that increasing client count or model size could favor SL, while increasing the number of data samples with client count and model size relatively low could favor FL. In a described healthcare-like setting with few clients and large models, the approaches were roughly comparable in some cases; FL was favored for larger datasets in a specified case. These are workload-dependent results from that analysis, not a universal ranking.
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For a deployment decision, calculate or measure total bytes in both directions over a complete training run, including retries and control traffic. Also measure the number of network round trips and end-to-end training duration: equal byte totals can have very different consequences on a high-latency or unreliable connection.
Is one approach more private?
Not by default. In their basic forms, both FL and SL keep raw examples at the client, but both transmit derived information: FL sends model updates, while SL sends intermediate activations. “Data stays local” does not establish that those transmissions reveal no information about the examples.
Define the threat model before comparing privacy: identify who receives updates or activations, what an attacker can access, whether the server is trusted, and what information must be protected. Then evaluate protections appropriate to that model, such as secure aggregation or noise mechanisms, along with transport security. These controls have their own implementation and utility trade-offs; their presence and effect depend on the actual design.
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The SplitFed paper describes differential privacy and PixelDP extensions, showing that additional mechanisms can be considered in a hybrid design. It does not mean every FL, SL, or SplitFed implementation includes those protections or offers the same privacy guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What about SplitFed and other hybrids?
SplitFed combines split learning’s client/server model partition with federated learning across clients. It is an option when a design needs both partitioning and cross-client federation, but it adds coordination and has its own privacy and robustness trade-offs.
The SplitFed paper reports similar test accuracy and communication efficiency to SL, alongside significantly lower computation time per global epoch than SL for multiple clients in its experiments. Treat this as a result for the paper’s configurations, data partitions, and threat models—not a promise for another implementation. A hybrid should be measured against FL and SL baselines on the same workload.
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What published edge-device results do—and do not—show
In its 2024 smart-meter evaluation, the Nature Communications paper reports that its proposed on-device training method achieved a 15.2× smaller meter memory footprint with similar accuracy compared with the benchmark methods in that evaluation. It also reports 22.4× memory-footprint savings, 2.02× communication-overhead savings, and 19.23× training-time savings against its specified conventional methods. These are results for that paper’s method and comparison, not general savings for SL over FL.
The same paper reports a maximum 2.97× shorter training time for its efficiency-optimal split strategy across four configurations of edge-server and smart-meter compute. The figure is bounded to those configurations; a different cut, device, server, or network can change the result.
“FedML: A Research Library and Benchmark for Federated Machine Learning,” an arXiv paper from 2020, identifies Android smartphones, Raspberry Pi 4, and NVIDIA Jetson Nano among its real-hardware testbeds, as well as on-device, distributed, and single-machine simulation paradigms. These are platforms and paradigms used in that paper, not assurance of compatibility with current releases or evidence that any one platform is sufficient for a particular model.
How to choose for your workload
Compare FL with one or more SL cut points using the same model, client data split, device mix, and network trace. Record accuracy alongside resource use and end-to-end cost rather than selecting by architecture label alone.
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- Set the workload and success criteria. Specify the model, examples per client, number of clients, data imbalance, participation pattern, target accuracy, and acceptable training time.
- Establish device limits. Measure peak training memory, client compute, and energy where measurable. Determine whether the complete model can run on the least-capable representative device.
- Measure network behavior. Record upload and download bytes per example and round or step, round trips, latency, packet loss, availability, and retransmissions on representative links.
- Test FL and candidate SL cuts. Keep data partitions and evaluation conditions fixed. For SL, include cut points with different client-side model sizes and activation payloads.
- Compare outcomes and operations. Report accuracy, convergence, wall-clock training time, peak device memory, client compute, total transferred bytes, and energy where measurable. Include server capacity, client churn, version compatibility, and the coordination burden.
- Evaluate privacy against the threat model. Inspect what updates or activations expose and assess the protections applied to them; do not infer privacy from raw-data locality alone.
Use the results to choose the least complex approach that meets the device, network, performance, privacy, and operational requirements. If neither baseline meets them, investigate a different cut, model, training schedule, or hybrid and benchmark it under the same conditions.
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