# FedSat-LAM: Enabling Large AI Models on Resource-Constrained Satellites via Hierarchical Federated Learning

Hannaneh B Pasandi, Zhaowei Tan, Mohammad Hosseini, Sina Darabi, Franck Rousseau, Juan Andrés A Fraire

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Hannaneh B Pasandi, Zhaowei Tan, Mohammad Hosseini, Sina Darabi, Franck Rousseau, et al. FedSat-LAM: Enabling Large AI Models on Resource-Constrained Satellites via Hierarchical Federated Learning. ACM MobiCom 2025 - 31st Annual International Conference on Mobile Computing and Networking, Nov 2025, Hong Kong, China. pp.19-24, 10.1145/3737902.3768355. hal-05423924

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RESEARCH-ARTICLE

FedSat-LAM: Enabling Large AI Models on Resource-Constrained
Satellites via Hierarchical Federated Learning

HANNANEH BARAHOUEI PASANDI, University of California, Berkeley, Berkeley, CA,
United States

ZHAOWEI TAN, University of California, Riverside, Riverside, CA, United States

MOHAMMAD HOSSEINI, Shahid Beheshti University, Tehran, Tehran, Iran

SINA DARABI

FRANCK ROUSSEAU, Grenoble Alpes University, Saint Martin d'Heres, Auvergne-Rhone-Alpes, France

JUAN ANDRES FRAIRE, INRIA Institut National de Recherche en Informatique et en Automatique, Le Chesnay, Ile-de-France, France

Open Access Support provided by:

INRIA Institut National de Recherche en Informatique et en Automatique

Shahid Beheshti University

University of California, Berkeley

University of California, Riverside

Grenoble Alpes University

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Published: 04 November 2025

Citation in BibTeX format

ACM MobiCom '25: The 31st Annual
International Conference on Mobile
Computing and Networking

November 4 - 8, 2025

Hong Kong, China

Conference Sponsors:
SIGMOBILE
# FedSat-LAM

## Enabling Large AI Models on Resource-Constrained Satellites via Hierarchical Federated Learning

Hannaneh B. Pasandi

UC Berkeley
Berkeley, CA, USA
h.pasandi@berkeley.edu

Zhaowei Tan

UC Riverside
ztan@ucr.edu

Mohammad Hosseini

Shahid Beheshti University
m-hosseini@sbu.ac.ir

Sina Darabi

USI
sina.darabs@usi.ch

Franck Rousseau

Grenoble Alps University
franck.rousseau@
univ-grenoble-alpes.fr

Juan A. Fraire

INRIA
juan.fraire@inria.fr

## ABSTRACT

LEO satellites generate 5.2 PB/day but can only downlink 2.3 PB/day, leaving 57% of data inaccessible. We present FedSat-LAM, enabling 632M-parameter foundation models on heterogeneous satellite constellations through: (1) multi-hop offloading reducing latency 41%, (2) tree-based aggregation achieving O(log N) complexity, and (3) PEFT training only 0.13% of parameters at 10.45 J/sample. Validated on 100-satellite simulations and Jetson hardware, we achieve 87.6% accuracy, 5.2% above FedProx—while cutting communication 85% and energy 95%.

## CCS CONCEPTS

* Computer systems organization → Embedded systems; * Computing methodologies → Distributed artificial intelligence; * Information systems → Spatial-temporal systems;

## KEYWORDS

federated learning, satellite edge computing, foundation models, parameter-efficient fine-tuning, LEO constellations

<figure><img src="image_8.png" /></figure>

This work is licensed under Creative Commons Attribution International 4.0.

EdgeFM '25, November 4–8, 2025, Hong Kong, China

© 2025 Copyright is held by the owner/author(s).

ACM ISBN 979-8-4007-1979-0/25/11

https://doi.org/10.1145/3737902.3768355

## ACM Reference Format

Hannaneh B. Pasandi, Zhaowei Tan, Mohammad Hosseini, Sina Darabi, Franck Rousseau, and Juan A. Fraire. 2025. FedSat-LAM: Enabling Large AI Models on Resource-Constrained Satellites via Hierarchical Federated Learning. In 2nd International Workshop on Edge and Mobile Foundation Models (EdgeFM '25), November 4–8, 2025, Hong Kong, China. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3737902.3768355

## 1 INTRODUCTION

Earth observation satellites face a fundamental data bottleneck: modern sensors generate data 2.3× faster than downlink capacity. The Sentinel-2 constellation produces 2.4 TB daily per satellite but transmits only 6 GB during 8-minute ground station passes at 100 Mbps average rates [15]. Recent surveys examine direct-to-device satellite communications and machine learning applications in satellite IoT [3, 14], while emerging work explores orbital edge intelligence for Earth observation [1]. This motivates on-board AI processing, yet deploying foundation models like SpectralGPT (≈632M parameters) on satellites faces severe constraints: CubeSats operate with 100-500 MFLOPS processors and 256 MB-4 GB RAM, while models require 2.4 GB memory and 24.7 GFLOPs per inference [4, 9].

### Daily Capacity Formulas

$$C_{\text{comm}} = \frac{\text{Bandwidth} \times T_{\text{contact}} \times N_{\text{passes}}}{8 \text{ bits/byte}} \qquad (1)$$

$$C_{\text{compute}} = \min \left( \frac{P_{\text{AI}} \times 86400 \text{ s}}{E_{\text{sample}}}, \frac{86400 \text{ s}}{t_{\text{inference}}} \right) \qquad (2)$$

where capacity is energy-bound if $P_{\text{AI}} \times 86400/E_{\text{sample}} < 86400/t_{\text{inference}}$.

Figure 1 reveals the data crisis motivating our work. The key insight is that data generation grows exponentially at 47% annually (blue curve) while downlink capacity increases only 21% linearly (red), creating an accumulated backlog (green) reaching 5.2 PB daily by 2024. Traditional centralized
<figure><img src="image_9.png" /><figcaption>Figure 1: Satellite Data Crisis (2018-2024): Generation outpaces transmission 2.3×, stranding 57% of data. Green: 5.2 PB daily backlog by 2024.</figcaption></figure>

processing is fundamentally unsustainable—57% of collected data becomes inaccessible, necessitating on-board processing capabilities. **The Computational Heterogeneity Crisis.** Satellite capabilities vary by three orders of magnitude following Pareto distribution (α = 1.2) based on our analysis of 147 operational satellites [4]. Flagship missions employ 4 TOPS processors (ESA's Φ-Sat-2 [6]), while 78% use 100-500 MFLOPS radiation-hardened processors. Power constraints limit computation to 10-20 W with thermal throttling reducing performance 40-60% after sustained operation [16].

**Energy Constraints.** CubeSat power budgets allocate only 20 W for computation from 50-200 W total solar generation [17]. Processing one 224×224 image consumes 15-45 W on embedded GPUs, exhausting daily energy budgets in hours [5]. This creates a fundamental barrier to foundation model deployment.

Our Contributions includes: 1) **Multi-hop offloading algorithm** with empirically validated load balancing achieving 67% straggler impact reduction through minimum-cost flow formulation. 2) **Tree-based hierarchical aggregation** achieving O(log N) complexity versus O(N) for ring protocols, reducing communication rounds from 100 to 8 for typical constellations. 3) **Energy-aware PEFT strategy** consuming 10.45 J/sample within CubeSat budgets while maintaining 87.6% accuracy on satellite imagery tasks. 4) **Contact-plan-aware evaluation framework** incorporating SGP4 orbital propagation, ISL handover modeling, and hardware validation on embedded systems.

## 2 SYSTEM MODEL

**Network Architecture.** We consider N satellites in Walker Delta constellation [19] with empirically measured ISL characteristics: i) **Intra-orbit optical ISLs.** 10 Gbps based on published Starlink specifications [2], ii) **Inter-orbit optical ISLs.** 1-4 Gbps varying with orbital separation [7], and iii) **Ground links.** 100-800 Mbps during 8-12 minute visibility

<figure><img src="image_10.png" /><figcaption>Figure 2: FedSat-LAM architecture: heterogeneous satellites (4 TOPS/500 MFLOPS/100 MFLOPS) connected via 10 Gbps intra-orbit and 1-4 Gbps inter-orbit ISLs for hierarchical aggregation.</figcaption></figure>

windows [15]. Figure 2 illustrates our hierarchical constellation topology. The natural hierarchy (intra-orbit → inter-orbit → ground) maps perfectly to tree-based federated aggregation, enabling efficient local aggregation through structured intra-orbit binary trees while sporadic inter-orbit links handle global synchronization, reducing communication by 43%.

**Heterogeneity Model.** We model computational heterogeneity using a Pareto distribution based on empirical satellite data:

$$P(C > c) = \left( \frac{c_{min}}{c} \right)^{\alpha}, \quad c \ge c_{min} \qquad (3)$$

where c represents computational capability (FLOPS), $c_{min}$ = 100 MFLOPS, and α = 1.2 from our analysis of 147 operational satellites. This yields: 20% high-capability satellites (4 TOPS), 30% medium-capability (500 MFLOPS), and 50% CubeSats (100 MFLOPS)

**Energy-Aware Problem Formulation.** Given local datasets {$D_i$}$_{i=1}^N$ with non-IID distributions $p_i(x, y)$ where KL divergence $D_{KL}(p_i||p_j) > 2.0$ between polar and equatorial satellites, we formulate the optimization problem:

$$\min_{w} F(w) = \sum_{i=1}^{N} \frac{|D_i|}{|D|} F_i(w) + \lambda E_{total} \qquad (4)$$

subject to:

$$E_{i}^{comp} + E_{i}^{comm} \le E_{i}^{budget} \quad \forall i \in [N] \qquad (5)$$

$$\sum_{j \in N_i} f_{ij} \le C_i^{comp} \quad \forall i \in [N] \qquad (6)$$

$$\sum_{(i,j) \in \mathcal{E}} b_{ij} \le B_{ij} \quad \forall \text{ ISL links} \qquad (7)$$

where $E_{total} = \sum_i (E_i^{comp} + E_i^{comm})$ with computation energy $E_i^{comp} = P_i \cdot t_i$ and communication energy $E_i^{comm} = \beta \cdot b_i$ for transmitted bits $b_i$, where we define $\beta = 10 \ \mu J/\text{bit}$ as the energy per transmitted bit (typical for RF transceivers
### Tree-based vs Ring Aggregation Comparison

<figure><img src="image_11.png" /><figcaption>Figure 3: Tree vs ring aggregation: (a) Binary tree with 4 levels, (b) Sequential ring O(N), (c) Tree O(log N) reduces rounds from 100 to 8.</figcaption></figure>

based on link budget: 20 W transmit power ÷ 2 Gbps ≈ 10 µJ/bit [11].

## 3 FEDSAT-LAM FRAMEWORK

**Multi-hop Flow-based Offloading.** We formulate computation offloading as a minimum-cost flow problem with load balancing:

$$ \min_{f_{ij}} \sum_{(i,j) \in E} c_{ij} f_{ij} + \kappa \cdot \max_j \{W_j\} \quad (8) $$

where: i) $f_{ij}$: computation flow from satellite i to j, ii) $c_{ij} = d_{ij}/b_{ij}$: cost (delay/bandwidth ratio), iii) $W_j = W_j^{orig} - \sum_k f_{jk} + \sum_i f_{ij}$: final workload, and iv) $\kappa = 0.3$: balances efficiency versus load distribution

<caption>**Algorithm 1: Greedy Multi-hop Offloading**</caption>

1: **Input:** Satellites S, capabilities C, topology G
2: **Output:** Offloading decisions {$f_{ij}$}
3: Initialize $W_i \leftarrow$ local workload for all $i \in S$
4: **while** $\exists i: W_i > \tau_i$ **do**
5:     $i^* \leftarrow \arg\max_i (W_i - \tau_i)$ // Most overloaded
6:     $N \leftarrow$ k-hop neighbors of $i^*$ sorted by $c_{ij}$
7:     **for** $j \in N$ **do**
8:         **if** $W_j < \tau_j$ **then**
9:             $f_{ij}^* \leftarrow \min(W_{i^*} - \tau_{i^*}, \tau_j - W_j)$
10:         Update $W_{i^*}, W_j$
11:     **end if**
12: **end for**
13: **end while**

**Empirical Performance.** Our greedy algorithm achieves load balancing within 15% of optimal min-max workload across 1000 random constellation instances, with 67% straggler delay reduction compared to baseline allocation.

**Tree-based Hierarchical Aggregation.** We implement binary reduction trees achieving O(log N) communication complexity:

Figure 3 demonstrates the complexity advantage. For a 100-satellite constellation with 10 satellites per orbit across 10 orbits:

**Phase 1 (Intra-orbit).** Binary tree aggregation within each orbit

$$ T_{\text{intra}} = \lceil \log_2(S) \rceil = \lceil \log_2(10) \rceil = 4 \text{ rounds} \quad (9) $$

### Measured Power Trace with CubeSat Projection (30× Slowdown Factor)

<figure><img src="image_12.png" /><figcaption>Figure 4: Measured power trace on Jetson Orin NX during inference + PEFT, projected to CubeSat using 30× slowdown. Upper: power phases totaling 10.45 J/sample. Lower: cumulative energy within 50 J budget.</figcaption></figure>

**Phase 2 (Inter-orbit).** Aggregation across orbit representatives

$$ T_{\text{inter}} = \lceil \log_2(R) \rceil = \lceil \log_2(10) \rceil = 4 \text{ rounds} \quad (10) $$

**Total complexity.** $O(\log N) = 8$ rounds versus $O(N) = 100$ rounds for sequential ring aggregation.

**Energy-Efficient PEFT Strategy.** We employ parameter-efficient fine-tuning to meet CubeSat constraints:

<table><caption>**Table 1: PEFT Memory and Computation Breakdown**</caption><thead><tr><th>Component</th><th>Parameters</th><th>Memory (MB)</th></tr></thead><tbody><tr><td>Frozen Encoder (Flash)</td><td>631.0M</td><td>2,524.0</td></tr><tr><td>Embedding Layer</td><td>50K</td><td>0.2</td></tr><tr><td>Classification Head</td><td>770K</td><td>3.1</td></tr><tr><td><strong>Total Trainable</strong></td><td><strong>820K (0.13%)</strong></td><td><strong>3.3</strong></td></tr><tr><td>Peak Activations (RAM)</td><td>-</td><td>12.0</td></tr><tr><td><strong>Total RAM Required</strong></td><td>-</td><td><strong>15.3</strong></td></tr></tbody></table>

Energy consumption per sample (with corrected calculation):

$$ E_{\text{PEFT}} = \underbrace{1.5 \text{ W} \times 5.9 \text{ s}}_{\text{CubeSat Inference}} + \underbrace{2.0 \text{ W} \times 0.8 \text{ s}}_{\text{Gradient}} = 8.85 + 1.6 = 10.45 \text{ J/sample} \quad (11) $$

This projection accounts for 30× computational slowdown on radiation-hardened processors (197 ms Jetson inference × 30 = 5.9 s CubeSat inference) while maintaining energy efficiency within the 50 J/sample CubeSat budget.

## 4 EXPERIMENTAL EVALUATION

**Experimental Setup. Datasets.** EuroSAT [8] (27,000 images, 10 classes) and UC Merced Land Use [21] (2,100 images, 21 classes) for satellite imagery classification.
<table><caption>**Table 2: Experimental Hyperparameters**</caption><thead><tr><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td>Optimizer</td><td>AdamW</td></tr><tr><td>Learning rate</td><td>1e-4</td></tr><tr><td>Local steps (K)</td><td>5</td></tr><tr><td>Batch size</td><td>32</td></tr><tr><td>Dirichlet α</td><td>{0.01, 0.1, 1.0, 10.0}</td></tr><tr><td>Quantization</td><td>8-bit</td></tr><tr><td>Momentum</td><td>0.9</td></tr><tr><td>Weight decay</td><td>1e-5</td></tr><tr><td>Communication rounds</td><td>200 (with early stopping)</td></tr></tbody></table>

**Baselines.** FedAvg [13], FedProx [12], FedNova [20], and SCAFFOLD [10].

**Contact-Plan-Aware Simulation.** We conduct trace-driven simulation incorporating realistic orbital dynamics:

**Orbital propagation.** SGP4 [18] with NORAD TLE data for 100-satellite Walker constellation (550 km altitude, 53° inclination)

**ISL handovers.** 4-7 minute connection cycles with 2-3 s re-establishment gaps based on beam steering limitations

**Communication modeling.** Doppler shifts ±200 kHz for RF ground links, store-and-forward buffering (50 MB gradient buffer per satellite)

**Ground contact windows.** 8-12 minute passes, 4 opportunities/day/satellite based on ground station locations

**Non-IIDness.** Dirichlet($\alpha$) allocation simulating geographic data distribution

**Hardware Validation Platform.** We validated core components on NVIDIA Jetson Orin NX with power constraints emulating CubeSat operation. Energy projections from Jetson to CubeSat are calculated using a 30× slowdown factor based on the ratio of commercial GPU to radiation-hardened processor performance (15 W Jetson ÷ 1.5 W CubeSat power limit × efficiency difference):

<table><caption>**Table 3: Hardware Measurements with CubeSat Power/Thermal Constraints**</caption><thead><tr><th>Metric</th><th>Jetson (15 W)</th><th>CubeSat Projection (1.5 W)</th></tr></thead><tbody><tr><td>Inference (224×224)</td><td>197 ms</td><td>5.9 s (30× slower)</td></tr><tr><td>Gradient computation</td><td>27 ms</td><td>0.8 s</td></tr><tr><td>Energy/sample</td><td>3.0 J</td><td>10.45 J</td></tr><tr><td>Peak power</td><td>15 W</td><td>1.5 W (throttled)</td></tr><tr><td>Model memory (Flash)</td><td>2.4 GB</td><td>2.4 GB</td></tr><tr><td>PEFT gradient (RAM)</td><td>3.3 MB</td><td>3.3 MB</td></tr><tr><td>Activations (RAM)</td><td>12 MB</td><td>12 MB</td></tr><tr><td><strong>Total RAM usage</strong></td><td><strong>15.3 MB</strong></td><td><strong>15.3 MB</strong></td></tr></tbody></table>

The 30× slowdown accounts for power throttling (15 W → 1.5 W) and radiation-hardened processor efficiency differences.

**Accuracy and Convergence Analysis.**

<table><caption>**Table 4: Comparison with FL Baselines on EuroSAT ($\alpha$ = 0.1)**</caption><thead><tr><th>Method</th><th>Acc. (%)</th><th>Rounds</th><th>Energy/R (J)</th><th>Bytes/R (MB)</th></tr></thead><tbody><tr><td>FedAvg</td><td>82.4±1.2</td><td>156</td><td>295.6</td><td>22.4</td></tr><tr><td>FedProx</td><td>82.4±0.9</td><td>145</td><td>301.2</td><td>22.4</td></tr><tr><td>FedNova</td><td>83.1±1.1</td><td>138</td><td>298.7</td><td>22.4</td></tr><tr><td>SCAFFOLD</td><td>84.7±0.8</td><td>121</td><td>312.4</td><td>44.8</td></tr><tr><td><strong>Ours</strong></td><td><strong>87.6±0.6</strong></td><td><strong>67</strong></td><td><strong>13.75</strong></td><td><strong>3.28</strong></td></tr><tr><td>vs FedProx</td><td>+5.2%</td><td>-54%</td><td>-95%</td><td>-85%</td></tr></tbody></table>

<figure><img src="image_13.png" /><figcaption>**Figure 5: (a) FedSat-LAM achieves 85.2% accuracy at extreme heterogeneity ($\alpha$=0.01) vs FedAvg's 69.1%. (b) Final accuracy across heterogeneity levels.**</figcaption></figure>

Table 4 demonstrates FedSat-LAM's superiority across all metrics, achieving 5.2% higher accuracy than FedProx while dramatically reducing communication and energy requirements. Figure 5 demonstrates robustness to non-IID data. At extreme heterogeneity ($\alpha$=0.01), FedSat-LAM maintains 85.2% accuracy versus 69.1% for FedAvg—a 16.1% improvement through hierarchical aggregation preventing divergence.

# 5 THEORETICAL CONVERGENCE ANALYSIS.

**Theorem 1 (Energy-Aware Convergence).** For L-smooth, $\mu$-strongly convex objectives with energy budget $E_{max}$ and communication rounds T:

$$ \mathbb{E}[F(w^T) - F^*] \le \frac{2L}{\mu T} + \frac{3L\Delta^2}{\mu} + \frac{C_L \cdot E_{max}}{N \cdot P_{avg} \cdot T} \quad (12) $$

where $\Delta = \max_i ||\nabla F_i(w) - \nabla F(w)||^2$ quantifies data heterogeneity (empirically $\Delta \approx 2.1/\alpha$), and $C_L = 0.1$ is a calibration constant.

**Proof sketch:** Standard FL convergence analysis extended with energy constraints. The third term bounds accuracy degradation from power-limited gradient computation, where quantization introduces $O(E_{max}^{-1})$ error per round.
<figure><img src="image_14.png" /><figcaption>Figure 6: (a) Delays cut 41% via offloading/tree aggregation. (b) FedSat-LAM scales linearly (12% at $ρ=0.9$) vs FedAvg exponential (38%). (c) Post-offloading load balance.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Figure 7: (a) Processing delay by satellite category. CubeSats achieve maximum 42% reduction (850 s→490 s) through offloading to higher-capability satellites. (b) Resource utilization efficiency showing improved load balancing after offloading.</figcaption></figure>

<table><caption>Table 5: Communication Overhead Analysis</caption><thead><tr><th>Component</th><th>Size (MB)</th><th>Quantized (MB)</th><th>Reduction</th></tr></thead><tbody><tr><td>Full model gradients</td><td>2,528.0</td><td>632.0</td><td>75%</td></tr><tr><td>PEFT gradients</td><td>3.28</td><td>0.82</td><td>75%</td></tr><tr><td><strong>FedSat vs Full</strong></td><td><strong>0.13%</strong></td><td><strong>0.13%</strong></td><td><strong>99.87%</strong></td></tr></tbody></table>

## System Performance Analysis.

Figure 6 reveals system-level improvements supporting our 41% latency reduction claim:

* Computation: $450 \text{ s} \rightarrow 180 \text{ s}$ (60% reduction via offloading)

* Aggregation: $120 \text{ s} \rightarrow 45 \text{ s}$ (63% reduction via trees)

* Synchronization: $80 \text{ s} \rightarrow 60 \text{ s}$ (25% reduction)

## Communication Efficiency Analysis.

<figure><img src="image_16.png" /><figcaption>Figure 8: (a) FedSat-LAM $O(\log N)$ scaling vs $O(N)$ ring protocols. (b) 99.9% communication reduction at 1000 satellites via PEFT and tree aggregation.</figcaption></figure>

Each PEFT round transmits only 0.82 MB with 8-bit quantization, compared to 632 MB for the full model—a 99.87% reduction enabling feasible satellite federation.

**Scalability Analysis.** Figure 8 demonstrates FedSat-LAM's scalability to mega-constellations. For 1000 satellites, FedSat-LAM requires only 10 communication rounds versus 1000 for ring protocols, while reducing total communication volume by 99.9% through PEFT and hierarchical aggregation.

## Ablation Studies.

<table><caption>Table 6: Component Ablation on EuroSAT Dataset ($α = 0.1$)</caption><thead><tr><th>Configuration</th><th>Accuracy (%)</th><th>Energy/Round (J)</th><th>Rounds</th></tr></thead><tbody><tr><td>Full FedSat-LAM</td><td>87.6</td><td>13.75</td><td>67</td></tr><tr><td>w/o offloading</td><td>79.3 (-8.3%)</td><td>17.25</td><td>89</td></tr><tr><td>w/o tree aggregation</td><td>85.1 (-2.5%)</td><td>23.65</td><td>134</td></tr><tr><td>w/o PEFT (full fine-tuning)</td><td>88.1 (+0.5%)</td><td>297.1</td><td>67</td></tr><tr><td>w/o quantization</td><td>87.8 (+0.2%)</td><td>55.0</td><td>67</td></tr><tr><td>Random offloading</td><td>82.4 (-5.2%)</td><td>15.50</td><td>78</td></tr><tr><td>Ring aggregation</td><td>86.2 (-1.4%)</td><td>31.20</td><td>156</td></tr></tbody></table>

Table 6 validates each component's necessity:

* Offloading provides largest accuracy gain (8.3%)

* Tree aggregation reduces rounds by 50%

* PEFT saves 95% energy with minimal accuracy loss

* Quantization further reduces energy by 75%

# 6 LIMITATIONS AND CONCLUSION

Our simulation models handovers at coarse granularity; real satellite links experience complex dynamics requiring sophisticated modeling. Hardware validation uses commercial processors while space-qualified ones operate differently. We are developing a 3U CubeSat testbed for ISS deployment in 2026. FedSat-LAM enables 632M-parameter foundation models on resource-constrained satellites through multi-hop offloading, tree-based aggregation, and PEFT. We achieve
87.6% accuracy—5.2% higher than FedProx—with 85% less communication and 10.45 J/sample energy consumption, providing the first practical framework for large AI models in space-based edge computing.

**Inter-Satellite Link Dynamics.** Our simulation models handovers at coarse granularity (4-7 minute cycles). Real satellite links experience complex dynamics including Doppler shifts, pointing errors, and atmospheric effects requiring sophisticated PHY modeling.

**Hardware Validation Fidelity.** While Jetson validation provides insights, space-qualified processors like BAE RAD750 operate at 200 MHz with different architectural efficiency than commercial processors.

**Future Deployment.** We are developing a 3U CubeSat testbed for ISS deployment in 2026 through NASA's CubeSat Launch Initiative, integrating Coral Edge TPU for space-rated inference acceleration.

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