Split Machine Learning Processes

Updated 26 December 2025

Split machine learning processes are distributed paradigms that partition an ANN at intermediate layers to balance computation load and preserve data privacy.
They incorporate diverse architectures—including serial, parallel, and multihop splits—to optimize resource allocation and reduce training time.
Advanced protocols integrate encryption, blockchain audit, and differential privacy to secure intermediate activations and ensure robust error resilience.

Split machine learning processes—often referred to as split learning (SL)—encompass a class of distributed machine learning paradigms in which an artificial neural network (ANN) is vertically partitioned at one or more intermediate layers, such that computation of each model segment is assigned to a distinct device or node. Intermediate activation tensors, rather than raw data or full model parameters, are transmitted between nodes, enabling collaborative training or inference while mitigating device resource constraints and preserving data privacy.

1. Conceptual Foundations and Comparison with Distributed and Federated Learning

Traditional distributed machine learning employs either data-parallel or model-parallel strategies. In data-parallel SGD, each worker hosts a replica of the full model and synchronizes gradients via a central parameter server or collective communication; model-parallel training shards the network across devices but is typically limited to single-datacenter settings with high bandwidth, since all model parameters or data must ultimately be accessible for full end-to-end backpropagation.

Federated learning (FL) moves full-model training to edge devices (clients), which download the current global weights, perform local stochastic optimization on private local data, and upload full-model updates to a server, where aggregation (e.g., FedAvg) computes the next global model (Tirana et al., 31 Jan 2024). This avoids raw data transmission but imposes significant memory and computation requirements on clients—creating bottlenecks on resource-constrained hardware (e.g., mobile, IoT).

In split learning, a global ANN is “cut” at one or more intermediate layers (“split points” or “cut layers”). Each client retains only the “head” (front layers), computes forward activations on local data, and sends those activations (“smashed data”) to another node, typically a resource-rich server or cloud node, which holds the “tail” (remaining layers). The server continues the forward and backward pass, computes the loss and gradients, and sends the resulting activations’ gradients back to the client for completion of backpropagation. SL thus lowers client-side RAM/computation, while restricting the exposure of raw data and model parameters (Tirana et al., 31 Jan 2024).

2. System Architectures: Serial, Parallel, and Multihop Split Learning

The foundational SL protocol is serial and sequential: one client at a time interacts with the server, resulting in poor scalability as additional clients increase queuing delays (Wang et al., 23 Nov 2025). To address this, recent innovations include:

Parallel Split Learning (PSL): Clients offload the central block of the model (“Part-2”) to a compute helper, which processes multiple client requests in parallel. Each helper maintains a distinct parameter copy per client, and batches forward/backward computations to reduce makespan over all clients (Tirana et al., 1 Feb 2024). The client model is divided into three segments: client-side (front), compute-node (middle), client-side (tail).
Multihop Parallel Split Learning (MP-SL): Generalizes PSL to partition the model into H segments and allocate them to a pipeline of nodes. Each intermediate node holds only its assigned segment, receiving and transmitting intermediate activations and gradients in a pipelined, bidirectional workflow. This architecture minimizes per-node memory (by distributing the model), supports effective pipeline parallelism, and enables efficient utilization of heterogeneous computing resources (Tirana et al., 31 Jan 2024).
Split-n-Chain: Further segments network layers across arbitrary computation chains (including multiple intermediates), decoupling trust assumptions and parameter knowledge, with blockchain-based auditability to provide verifiable logs of all forward outputs and gradients at each node (Sahani et al., 10 Mar 2025).
Communication-Computation Pipeline Parallel Split Learning (C²P²SL): In wireless edge regimes, splits the model at an appropriate layer between user equipment (UE) and base station (BS); divides each batch into micro-batches to pipeline forward and backward computations as well as uplink and downlink transmissions, thus hiding latency and reducing wall-clock training time by >38% compared to non-pipelined SL (Liu et al., 28 Nov 2025).

Table: Comparison of Major SL Variants

Variant	Model Partitioning	Scalability	Resource Distribution
Serial SL	Single split	Sequential, slow	Heavy on server/compute node
Parallel SL	Single/dual split	Parallelizable, but high memory at helper	Moderate per-node load
MP-SL	Multi-segment, pipelined	Highly scalable	Minimal per-node memory
Split-n-Chain	Arbitrary, chained	Flexible	Layer-assigned per node
C²P²SL	2-part with batching	Pipelined/edge	Pipelined compute/comm.

3. Training Protocols, Scheduling, and Optimization

Split learning demands meticulous orchestration of forward and backward passes, node assignments, and resource scheduling. In MP-SL, an explicit manager (orchestrator) selects cut layers, determines pipeline partitioning (formulated as an ILP for optimal split-point selection), and assigns nodes per segment. Each epoch proceeds as a round of potentially parallel client batches, with forward activations traversing the pipeline and backward gradients returning in reverse (Tirana et al., 31 Jan 2024).

Parallel SL and MP-SL must also optimize workflow for makespan minimization—the wall-clock completion time for all clients. The underlying optimization is NP-hard (shown via reduction from classic parallel machine scheduling) (Tirana et al., 1 Feb 2024). To address this, decomposition-based (ADMM) methods co-optimize client-helper assignments and batch scheduling; for large-scale regimes, balanced-greedy heuristics achieve near-optimal results at linear time complexity. Empirically, these strategies halve per-batch training makespan compared to naïve random assignment.

C²P²SL further demonstrates the necessity of jointly optimizing micro-batch size, number of pipeline stages, cut-layer choice, batch splits, and time-slot allocations to minimize system “bubble ratio” (fraction of idle time at the BS). Alternating optimization, combining convex and MILP subproblems, produces efficient solutions with robust wall-clock performance (Liu et al., 28 Nov 2025).

4. Split Point Selection and Error Resilience

The choice of split point is critical. “Shallow” splits (early layers) result in minimal client computation but large intermediate activations with higher privacy risk and vulnerability to transmission errors; “deep” splits shift more computation to the client and reduce privacy leakage (Shiranthika et al., 29 May 2024). In SplitFed learning, deep splits consistently outperform shallow ones in error resilience under simulated packet loss: Mean Jaccard Index (MJI) degrades more gracefully, with up to 4–5 percentage points better MJI at severe (P_L=0.9) loss rates.

Split-point optimization may thus be cast as a constrained maximization balancing client workload, privacy, and robustness, with empirical findings that up to 50% transmission loss can be tolerated with <3% accuracy degradation if ReLU activations and deep splits are used. In practice, split placement should maximize resilience ratio R(s; P_L, N_c), subject to resource and privacy constraints.

5. Privacy, Security, and Model Confidentiality

SL protocols restrict the exposure of raw data to external devices, as only intermediate activations (“smashed data”) are exchanged (Poirot et al., 2019). However, vanilla SL is susceptible to “visual invertibility” and feature-space hijacking attacks, as activations from shallow splits often retain recoverable structure about the input (Khan et al., 14 Apr 2024). To mitigate this, several privacy-enhancing extensions have been developed:

Homomorphic Encryption (HE): Clients encrypt activations (CKKS scheme) before transmission; servers evaluate linear layers homomorphically, then return encrypted activations for local decryption and classification. U-shaped SL with HE achieves near-identical accuracy (≤2.65% drop), at the cost of increased computational and communication overhead—substantially reduced by batching and careful parameter selection (Khan et al., 2023, Khan et al., 2023, Khan et al., 2023).
Function Secret Sharing (FSS): Clients mask activations with random values and distribute the computation to two non-colluding servers, each holding a share of the server-side function. FSS ensures that neither server can reconstruct the input or model, and prohibits model inversion or feature-space hijacking even under strong adversarial assumptions (Khan et al., 14 Apr 2024, Khan et al., 14 Jul 2025). U-shaped SL with FSS further secures training labels, as all classification is performed locally.
Blockchain Audit: Split-n-Chain augments distributed SL by logging all forward outputs and gradients as signed transactions on a permissioned blockchain. This enables on-chain verification of training correctness, data lineage, and tamper detection, without disclosing data or full model parameters (Sahani et al., 10 Mar 2025).
Differential Privacy: SplitFed (SFL) and similar protocols can add DP noise to both activations and gradients, achieving certified privacy-utility trade-offs and robustness against label reconstruction attacks (Yang et al., 2022, Thapa et al., 2020).

6. Performance, Empirical Evaluation, and Trade-offs

MP-SL and parallel SL approaches offer substantial memory and time savings over traditional FL or monolithic distributed learning, especially as the number of participating clients or the depth of the model scales:

MP-SL reduced Raspberry Pi 4 client RAM usage by up to 76% and compute-node memory requirements per segment from ~80 GB (one-hop VGG-19/100 clients) to ~20 GB (4-hop split) (Tirana et al., 31 Jan 2024).
Three-hop MP-SL provides ~2× acceleration versus one-hop parallel SL with 150 clients, due to pipelined execution.
Optimized split-point selection produced up to 19% faster epoch times over manual splits.
C²P²SL achieves the same test accuracy (≈91% on CIFAR-10) as conventional SL/PSL, with >38% reduction in wall-clock time under constrained bandwidth (Liu et al., 28 Nov 2025).
Split-n-Chain attains indistinguishable convergence curves from monolithic training, with only modest runtime increase attributable to extra communication hops and audit logging (Sahani et al., 10 Mar 2025).

Experimental studies confirm the error resilience of deep splits (SplitFed), the cost-efficiency of pipeline parallelization, and the scalability of workflow-optimized scheduling algorithms. U-shaped SL (privacy-preserving variant) maintains near-centralized accuracy even with 50 clients in healthcare imaging tasks (Poirot et al., 2019).

7. Limitations, Open Problems, and Future Directions

Key limitations of split learning include:

Communication overhead (frequent transfer of high-dimensional activations and gradients), especially for shallow cuts or vision models with large feature maps (Tirana et al., 31 Jan 2024).
Proper selection of split points must balance device compute with privacy and robustness to channel errors (Shiranthika et al., 29 May 2024).
Homomorphic encryption and FSS incur nontrivial computational overhead, though techniques such as packing and hybrid cryptography considerably mitigate this (Khan et al., 2023, Khan et al., 14 Apr 2024).
Some heavy models or tasks (e.g., VGG16 on CIFAR-10) may fail to converge in split or federated-split regimes without further regularization (Thapa et al., 2020).
Strong privacy guarantees require non-colluding servers (FSS) or secure audit via permissioned blockchain; these increase system complexity (Sahani et al., 10 Mar 2025, Khan et al., 14 Jul 2025).

Future research directions include extensions to more general topologies, automatic split-point tuning, integration with model compression/quantization, the synthesis of SL with reinforcement/adaptive learning, and further cryptographically secure protocols suitable for high-stakes domains such as healthcare or finance.

References:

(Tirana et al., 31 Jan 2024) "MP-SL: Multihop Parallel Split Learning" (Tirana et al., 1 Feb 2024) "Workflow Optimization for Parallel Split Learning" (Shiranthika et al., 29 May 2024) "Optimizing Split Points for Error-Resilient SplitFed Learning" (Poirot et al., 2019) "Split Learning for collaborative deep learning in healthcare" (Sahani et al., 10 Mar 2025) "Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability" (Khan et al., 2023, Khan et al., 2023, Khan et al., 2023) (U-shaped SL with Homomorphic Encryption) (Khan et al., 14 Apr 2024, Khan et al., 14 Jul 2025) (SL with Function Secret Sharing) (Liu et al., 28 Nov 2025) (C²P²SL: Communication-Computation Pipeline Parallel Split Learning) (Wang et al., 23 Nov 2025) (CycleSL: Scalable Split Learning) (Yang et al., 2022, Thapa et al., 2020) (Differential Privacy and SplitFed integration)