Interoperable Compression & Decompression ML Models

Updated 2 July 2025

Interoperable compression and decompression ML models are a set of techniques that enable efficient storage and transfer while preserving essential properties across various systems.
They leverage constrained optimization and modular architectures—using methods like quantization, pruning, and low-rank decomposition—to maintain functional equivalence and resource efficiency.
These methods yield practical benefits such as improved compression ratios, minimal accuracy loss, and enhanced runtime throughput in distributed, edge, and federated learning applications.

Interoperable compression and decompression ML models are a class of algorithms, frameworks, and design principles that enable ML models (or their data artifacts) to be efficiently compressed in a manner that supports seamless decompression, training, or inference across diverse systems, architectures, and application stages. The primary goal of these approaches is to facilitate storage, deployment, transmission, or collaborative computation of ML models and datasets, while ensuring that essential properties—such as functional equivalence, training compatibility, or resource efficiency—are preserved independent of platform or downstream usage scenario.

1. Core Principles and Mathematical Foundations

Central to interoperable compression/decompression is the abstraction of model compression as a constrained optimization or information-preserving transformation that separates “learning” from “compression logic”. Techniques such as the LC (Learning-Compression) algorithm (2005.07786) formalize this as a constrained optimization problem: $\min_{\mathbf{w}, \boldsymbol{\theta}} L(\mathbf{w}) \quad \text{s.t.} \ \mathbf{w} = \boldsymbol{\Delta}(\boldsymbol{\theta})$ where $L(\mathbf{w})$ is the loss for model weights $\mathbf{w}$ , the compression parameters $\boldsymbol{\theta}$ , and $\boldsymbol{\Delta}$ the decompression mapping. The LC algorithm alternates between:

Learning (L) step: Optimize $L(\mathbf{w}) + \frac{\mu}{2}\| \mathbf{w} - \boldsymbol{\Delta}(\boldsymbol{\theta}) \|^2$
Compression (C) step: Find $\boldsymbol{\theta}$ minimizing $\|\mathbf{w} - \boldsymbol{\Delta}(\boldsymbol{\theta})\|^2$ This systematic separation allows arbitrary combinations of models and compression schemes, underpinning interoperability.

Further generalizations span other modalities, such as time series prediction, where orthogonal key matrix frameworks enable channel-compressed/decompressed data to be processed by generic single-channel predictors and reconstructed efficiently (2506.00614).

2. Modular and Extendable Architectures

A key attribute of interoperable frameworks is modularity. In the LC software system (2005.07786), users specify which layers to compress, and how, via a dictionary-based interface, e.g.:

compression_tasks = {
  Param([l1.weight, l3.weight]): (AsVector, AdaptiveQuantization(k=6)),
  Param(l2.weight): (AsIs, LowRank(target_rank=3)),
}

New compression types are integrated by subclassing and defining a compress() method, with no changes required to the rest of the algorithm—a property that supports “plug-and-play” extensibility. This approach extends to data-centric ML pipelines, where BWARE enables morphing of compressed representations through feature engineering and transformations—eschewing decompression and allowing lossless transitions between optimized formats (2504.11067).

Additional frameworks employ architectural modularity at different system levels: for distributed/federated learning (2206.10452), auxiliary “shift” vectors decouple compression from reference point, allowing different workers to employ distinct strategies while maintaining theoretical consistency and compatibility; for time series and edge–cloud ML, modular compression and decompression blocks bracket a completely generic predictor (2506.00614).

3. Compression Methodologies and Interoperability Guarantees

A spectrum of interoperable compression techniques exists, each with distinct mathematical and practical properties:

Quantization: Ranges from layerwise k-means clustering of weights to bit-width assignment via dynamic programming/knapsack, with methods such as ELF (Exponent-Less Float) eliminating exponent redundancies for floating-point model parameters, yielding bit-exact reversibility with bounded error (2402.13429).
Pruning: Based on magnitude or structured selection (e.g., largest-magnitude weights or neuron/channel selection via interpolative decomposition), with the guarantee that layer structure and per-example predictions are preserved (see ID methods (2108.00065)).
Low-rank/Matrix/Tensor Decomposition: Layers are decomposed into smaller-rank products, with adaptable per-layer ranks inferred automatically. For lossless compression, optimal rank and noise bounds are derived via total differential analysis to ensure loss is not increased (LLC framework (2412.06868)).
Delta/Residual Approaches: Specialist architectures like D^2-MoE (2502.17298) split model parameters into a “shared base” (via information-weighted averaging) and “deltas” (compressible via SVD), supporting modular reconstruction and scalable compression.
Byte-level/Tensor Compression for Training Artifacts: For managing vast checkpoint data (e.g., in LLM training), LMC leverages byte-grouping, run-length encoding, and block-wise Huffman coding on incremental deltas, obtaining high throughput and matching entropy limits (2505.09810).
Predictive/Universal Coding: Some works recast prediction as compression (or vice versa), allowing any predictive model to be used as a lossless compressor (or conditional generative model) via arithmetic coding and associated log-probabilities (2309.10668).

Device and platform interoperability is often ensured through:

Complete structural preservation (no custom layers or operator changes),
Deterministic integer quantization and accumulation (2212.01330),
Universal latent-to-feature adaptation modules (e.g., ComNeck’s transform-neck bridges neural codecs and MLLMs (2407.19651)),
Standardized, invertible encoding/decoding procedures with published interfaces (e.g., ELF’s open specification).

4. Performance, Scaling, and Application Scenarios

Empirical evaluations across multiple architectures (CNNs, Transformers, MoE LLMs, time series predictors) and datasets show:

Compression Ratios: Improvements from 1.3× to 2.2× for storage (model files), up to 80% reduction in memory footprint for certain architectures, and up to 8.8× reduction in training time for data-centric pipelines (2506.22190).
Accuracy Preservation: Many frameworks achieve negligible loss (≤0.3% drop; sometimes improved loss) compared to uncompressed baselines. For certain class-preserving or interpolative decompositions, per-example decision consistency is maintained at >97% (2108.00065).
Runtime and Throughput: Parallel implementations of byte-grouped compressors reach throughput exceeding 2.7 GiB/s (2505.09810); quantization/decomposition search routines are optimized to minutes per model (2412.06868).
Robustness: Compression-aware quantization and pruning methods are designed to avoid “dead” weights or excessive sensitivity; decompression methods in BWARE and LMC handle morphological adaptation and incremental deltas without global decompression.
Deployment Scenarios: Applications include distributed/federated learning, edge device offloading (where bandwidth and memory are constrained), asynchronous checkpointing in cloud LLM training, and multi-vendor 6G wireless networks where encoder and decoder are trained separately with only data pair sharing required (2506.21796).

5. Challenges, Limitations, and Future Directions

Despite substantial progress, several interoperable compression topics remain active:

Handling Outliers: Methods like ELF may see diminished gains when models have more out-of-range parameters; adaptive, hybrid frameworks (e.g., Elves) address this by choosing among techniques per model/layer (2402.13429).
Generalization Boundaries: For ultra-sensitive or scientific ML tasks, the floating point error bounds may require formal certification or custom strategies (2402.13429).
Scalability and Automation: Automated detection of compression boundaries (LLC) and automatic per-layer adaptation (ID methods) continue to evolve; their extension to novel network types and massive scale is under exploration (2412.06868).
Interfacing Across Toolchains: Standardization of compressed artifact formats, APIs for decompression, and compatibility with orchestration tools or model registries (e.g., Hugging Face) is being advanced.
Data-Compression for Direct ML Training: Approaches like dreaMLearning propose training on entropy-deduplicated, representative compressed datasets without decompression (2506.22190), suggesting new frontiers in model and data pipeline interoperability.
Multi-vendor Confidentiality and Federation: Protocols for safe, non-disclosive sharing in collaborative ML (e.g., cross-vendor CSI feedback for 6G (2506.21796)) point to growing emphasis on privacy-preserving interoperability.

6. Comparative Table: Feature Matrix of Approaches

Framework/Technique	Compression Type(s)	Interoperability Mechanism	Application Scope
LC Algorithm	Quantization, Pruning, Low-rank	Modular L/C steps, plug-in C-steps	General neural models
Interpolative Decomposition	Channel/Neuron Pruning	Structure-preserving layer compression	DNNs (CV, NLP)
ELF/Elves	Float compression	IEEE format, auto adaptivity	PTM file storage, ML model registries
BWARE	Matrix/data compression	Morphing, trans-feature transform	Data-centric ML, feature engineering
LMC	Checkpoint delta compression	Byte-grouped blockwise coding	LLM training, high-frequency checkpoint
TransCompressor	Time series, sensor data	Prompt-engineered LLM reconstruction	Edge/cloud IoT, smart transport
Predictability-Aware (PCDF)	Multichannel time series	Periodic Toeplitz key design	Time series, MIMO, edge/cloud
Shifted Compression	Gradient/model transmission	Shared theory for shift compressors	Distributed/federated ML training
ComNeck	Image latents for MLLMs	Universal transform-neck, surrogate loss	Visual-language/edge-cloud vision
dreaMLearning	Data compression for ML	Entropy-driven dedup, weighted training	Tabular/image data, federated/edge

7. Significance and Outlook

Interoperable ML compression and decompression frameworks form a foundational layer for scalable, efficient, and reliable ML operations in a world of heterogeneous data, hardware, and deployment environments. Their modularity, theory-driven design, and emphasis on structural and functional preservation enables robust sharing, retraining, fine-tuning, and deployment of compressed models and datasets across platforms and organizational boundaries. The field is characterized by rapid innovation targeted at automation, standardization, and direct training compatibility, supporting the future of federated, distributed, and resource-constrained ML systems.